High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 25	93.63 22	74.77 128	92.29 7	95.97 2	74.28 30	97.24 13	88.58 30	96.91 1	94.87 18
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 61	95.06 1	94.23 3	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 17	96.68 2	94.95 12

PC_three_1452									68.21 279	92.02 12	94.00 56	82.09 5	95.98 57	84.58 64	96.68 2	94.95 12

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 68	93.57 8	94.06 11	77.24 60	93.10 1	95.72 8	82.99 1	97.44 7	89.07 22	96.63 4	94.88 16

IU-MVS					95.30 2	71.25 61		92.95 56	66.81 290	92.39 6				88.94 25	96.63 4	94.85 21

test_241102_TWO								94.06 11	77.24 60	92.78 4	95.72 8	81.26 8	97.44 7	89.07 22	96.58 6	94.26 52

test_0728_THIRD									78.38 38	92.12 9	95.78 4	81.46 7	97.40 9	89.42 17	96.57 7	94.67 29

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 54	82.45 3	96.87 20	83.77 75	96.48 8	94.88 16

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 13	96.44 9	94.41 42

No_MVS				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 13	96.44 9	94.41 42

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 28	92.85 60	80.26 11	87.78 42	94.27 41	75.89 19	96.81 23	87.45 41	96.44 9	93.05 120

DVP-MVS		89.60 3	90.35 3	87.33 41	95.27 5	71.25 61	93.49 10	92.73 65	77.33 57	92.12 9	95.78 4	80.98 9	97.40 9	89.08 20	96.41 12	93.33 103
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				87.71 32	95.34 1	71.43 60	93.49 10	94.23 3					97.49 4	89.08 20	96.41 12	94.21 53

ACMMP_NAP		88.05 17	88.08 18	87.94 19	93.70 41	73.05 22	90.86 60	93.59 24	76.27 92	88.14 35	95.09 19	71.06 67	96.67 29	87.67 38	96.37 14	94.09 58

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 46	94.10 9	75.90 98	92.29 7	95.66 10	81.67 6	97.38 11	87.44 42	96.34 15	93.95 66
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		87.67 22	87.72 22	87.54 36	93.64 44	72.04 50	89.80 84	93.50 26	75.17 117	86.34 61	95.29 17	70.86 69	96.00 55	88.78 28	96.04 16	94.58 34
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 66	71.95 51	92.40 25	94.74 2	75.71 100	89.16 23	95.10 18	75.65 21	96.19 47	87.07 43	96.01 17	94.79 23

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 65	93.00 47	80.90 7	88.06 37	94.06 52	76.43 16	96.84 21	88.48 33	95.99 18	94.34 48

PHI-MVS		86.43 46	86.17 53	87.24 42	90.88 95	70.96 70	92.27 33	94.07 10	72.45 182	85.22 71	91.90 109	69.47 85	96.42 40	83.28 79	95.94 19	94.35 47

test_prior2							88.85 125		75.41 107	84.91 75	93.54 69	74.28 30		83.31 78	95.86 20

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 74	72.96 25	93.73 5	93.67 21	80.19 12	88.10 36	94.80 23	73.76 34	97.11 15	87.51 40	95.82 21	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
ZNCC-MVS		87.94 19	87.85 21	88.20 12	94.39 24	73.33 19	93.03 15	93.81 18	76.81 74	85.24 70	94.32 39	71.76 55	96.93 19	85.53 54	95.79 22	94.32 49

9.14			88.26 16		92.84 65		91.52 51	94.75 1	73.93 150	88.57 29	94.67 25	75.57 22	95.79 59	86.77 45	95.76 23

DeepPCF-MVS	80.84 1	88.10 13	88.56 14	86.73 55	92.24 73	69.03 106	89.57 93	93.39 31	77.53 53	89.79 20	94.12 49	78.98 12	96.58 35	85.66 51	95.72 24	94.58 34

train_agg		86.43 46	86.20 50	87.13 45	93.26 52	72.96 25	88.75 131	91.89 106	68.69 271	85.00 73	93.10 81	74.43 27	95.41 76	84.97 56	95.71 25	93.02 122

test9_res														84.90 57	95.70 26	92.87 127

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 54	93.83 4	93.96 14	75.70 102	91.06 16	96.03 1	76.84 14	97.03 17	89.09 19	95.65 27	94.47 41
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MM		89.16 6	89.23 7	88.97 4	90.79 98	73.65 10	92.66 24	91.17 132	86.57 1	87.39 51	94.97 21	71.70 57	97.68 1	92.19 1	95.63 28	95.57 1

agg_prior2														82.91 84	95.45 29	92.70 131

CDPH-MVS		85.76 62	85.29 75	87.17 44	93.49 47	71.08 66	88.58 140	92.42 81	68.32 278	84.61 84	93.48 71	72.32 47	96.15 49	79.00 121	95.43 30	94.28 51

DeepC-MVS	79.81 2	87.08 37	86.88 42	87.69 33	91.16 87	72.32 45	90.31 74	93.94 15	77.12 66	82.82 117	94.23 44	72.13 51	97.09 16	84.83 60	95.37 31	93.65 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MTAPA		87.23 33	87.00 36	87.90 22	94.18 35	74.25 5	86.58 210	92.02 98	79.45 22	85.88 63	94.80 23	68.07 104	96.21 46	86.69 46	95.34 32	93.23 106

DeepC-MVS_fast	79.65 3	86.91 38	86.62 44	87.76 27	93.52 46	72.37 43	91.26 54	93.04 42	76.62 82	84.22 93	93.36 77	71.44 61	96.76 25	80.82 105	95.33 33	94.16 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		87.69 21	87.55 26	88.12 13	89.45 134	71.76 53	91.47 52	89.54 185	82.14 3	86.65 59	94.28 40	68.28 103	97.46 6	90.81 6	95.31 34	95.15 8

MP-MVS		87.71 20	87.64 23	87.93 21	94.36 26	73.88 6	92.71 23	92.65 71	77.57 49	83.84 102	94.40 36	72.24 49	96.28 43	85.65 52	95.30 35	93.62 90
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		87.37 31	87.25 32	87.73 28	94.53 17	72.46 40	89.82 82	93.82 17	73.07 174	84.86 78	92.89 88	76.22 17	96.33 41	84.89 59	95.13 36	94.40 44

balanced_conf03		86.78 39	86.99 37	86.15 66	91.24 86	67.61 153	90.51 65	92.90 57	77.26 59	87.44 50	91.63 119	71.27 64	96.06 50	85.62 53	95.01 37	94.78 24

GST-MVS		87.42 28	87.26 31	87.89 24	94.12 36	72.97 24	92.39 27	93.43 29	76.89 72	84.68 79	93.99 58	70.67 72	96.82 22	84.18 72	95.01 37	93.90 69

APD-MVS		87.44 26	87.52 27	87.19 43	94.24 32	72.39 41	91.86 41	92.83 61	73.01 176	88.58 28	94.52 27	73.36 35	96.49 38	84.26 68	95.01 37	92.70 131
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		88.06 15	88.01 19	88.24 11	94.41 22	73.62 11	91.22 57	92.83 61	81.50 5	85.79 65	93.47 73	73.02 42	97.00 18	84.90 57	94.94 40	94.10 57

ACMMPR		87.44 26	87.23 33	88.08 15	94.64 13	73.59 12	93.04 13	93.20 35	76.78 76	84.66 82	94.52 27	68.81 96	96.65 30	84.53 65	94.90 41	94.00 63

SPE-MVS-test		86.29 50	86.48 45	85.71 76	91.02 91	67.21 170	92.36 30	93.78 19	78.97 33	83.51 109	91.20 134	70.65 73	95.15 87	81.96 94	94.89 42	94.77 25

HFP-MVS		87.58 23	87.47 28	87.94 19	94.58 16	73.54 15	93.04 13	93.24 34	76.78 76	84.91 75	94.44 34	70.78 70	96.61 32	84.53 65	94.89 42	93.66 83

ZD-MVS					94.38 25	72.22 46		92.67 68	70.98 213	87.75 44	94.07 51	74.01 33	96.70 27	84.66 63	94.84 44

region2R		87.42 28	87.20 34	88.09 14	94.63 14	73.55 13	93.03 15	93.12 41	76.73 79	84.45 87	94.52 27	69.09 90	96.70 27	84.37 67	94.83 45	94.03 61

原ACMM1				84.35 122	93.01 62	68.79 113		92.44 78	63.96 335	81.09 141	91.57 122	66.06 128	95.45 71	67.19 247	94.82 46	88.81 282

HPM-MVS		87.11 35	86.98 38	87.50 39	93.88 39	72.16 47	92.19 34	93.33 32	76.07 95	83.81 103	93.95 61	69.77 82	96.01 54	85.15 55	94.66 47	94.32 49
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
NormalMVS		86.29 50	85.88 59	87.52 37	93.26 52	72.47 38	91.65 43	92.19 92	79.31 24	84.39 89	92.18 102	64.64 142	95.53 67	80.70 108	94.65 48	94.56 37

lecture		88.09 14	88.59 13	86.58 58	93.26 52	69.77 92	93.70 6	94.16 5	77.13 65	89.76 21	95.52 14	72.26 48	96.27 44	86.87 44	94.65 48	93.70 82

DPM-MVS		84.93 80	84.29 87	86.84 52	90.20 109	73.04 23	87.12 188	93.04 42	69.80 242	82.85 116	91.22 133	73.06 41	96.02 53	76.72 152	94.63 50	91.46 178

TSAR-MVS + MP.		88.02 18	88.11 17	87.72 30	93.68 43	72.13 48	91.41 53	92.35 83	74.62 132	88.90 26	93.85 64	75.75 20	96.00 55	87.80 37	94.63 50	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PGM-MVS		86.68 42	86.27 49	87.90 22	94.22 33	73.38 18	90.22 76	93.04 42	75.53 104	83.86 101	94.42 35	67.87 108	96.64 31	82.70 90	94.57 52	93.66 83

XVS		87.18 34	86.91 41	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 106	94.17 46	67.45 111	96.60 33	83.06 80	94.50 53	94.07 59

X-MVStestdata		80.37 174	77.83 211	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 106	12.47 449	67.45 111	96.60 33	83.06 80	94.50 53	94.07 59

test12				86.80 54	92.63 69	70.70 77		91.79 112		82.71 119		71.67 58	96.16 48		94.50 53	93.54 95

MVSMamba_PlusPlus		85.99 54	85.96 58	86.05 69	91.09 88	67.64 152	89.63 91	92.65 71	72.89 179	84.64 83	91.71 115	71.85 53	96.03 51	84.77 62	94.45 56	94.49 40

CP-MVS		87.11 35	86.92 40	87.68 34	94.20 34	73.86 7	93.98 3	92.82 64	76.62 82	83.68 105	94.46 31	67.93 106	95.95 58	84.20 71	94.39 57	93.23 106

CSCG		86.41 48	86.19 52	87.07 46	92.91 63	72.48 37	90.81 61	93.56 25	73.95 148	83.16 112	91.07 139	75.94 18	95.19 85	79.94 116	94.38 58	93.55 94

MSLP-MVS++		85.43 69	85.76 63	84.45 118	91.93 77	70.24 81	90.71 62	92.86 59	77.46 55	84.22 93	92.81 92	67.16 115	92.94 196	80.36 111	94.35 59	90.16 226

mPP-MVS		86.67 43	86.32 47	87.72 30	94.41 22	73.55 13	92.74 21	92.22 89	76.87 73	82.81 118	94.25 43	66.44 122	96.24 45	82.88 85	94.28 60	93.38 99

SD-MVS		88.06 15	88.50 15	86.71 56	92.60 71	72.71 29	91.81 42	93.19 36	77.87 42	90.32 18	94.00 56	74.83 23	93.78 149	87.63 39	94.27 61	93.65 87
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 17	94.11 7	80.27 10	91.35 14	94.16 47	78.35 13	96.77 24	89.59 15	94.22 62	94.67 29
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DELS-MVS		85.41 70	85.30 74	85.77 75	88.49 174	67.93 144	85.52 245	93.44 28	78.70 34	83.63 108	89.03 190	74.57 24	95.71 62	80.26 113	94.04 63	93.66 83
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
EPNet		83.72 95	82.92 108	86.14 68	84.22 304	69.48 97	91.05 59	85.27 289	81.30 6	76.83 218	91.65 117	66.09 127	95.56 64	76.00 158	93.85 64	93.38 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EC-MVSNet		86.01 53	86.38 46	84.91 104	89.31 143	66.27 183	92.32 31	93.63 22	79.37 23	84.17 95	91.88 110	69.04 94	95.43 73	83.93 74	93.77 65	93.01 123

3Dnovator+	77.84 4	85.48 67	84.47 86	88.51 7	91.08 89	73.49 16	93.18 12	93.78 19	80.79 8	76.66 223	93.37 76	60.40 210	96.75 26	77.20 142	93.73 66	95.29 6

reproduce-ours		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 120	88.96 24	95.54 12	71.20 65	96.54 36	86.28 48	93.49 67	93.06 118

our_new_method		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 120	88.96 24	95.54 12	71.20 65	96.54 36	86.28 48	93.49 67	93.06 118

CS-MVS		86.69 41	86.95 39	85.90 74	90.76 99	67.57 155	92.83 18	93.30 33	79.67 19	84.57 86	92.27 100	71.47 60	95.02 96	84.24 70	93.46 69	95.13 9

CANet		86.45 45	86.10 55	87.51 38	90.09 111	70.94 72	89.70 88	92.59 75	81.78 4	81.32 136	91.43 127	70.34 74	97.23 14	84.26 68	93.36 70	94.37 46

reproduce_model		87.28 32	87.39 30	86.95 50	93.10 58	71.24 65	91.60 45	93.19 36	74.69 129	88.80 27	95.61 11	70.29 76	96.44 39	86.20 50	93.08 71	93.16 113

新几何1				83.42 169	93.13 56	70.71 76		85.48 288	57.43 396	81.80 130	91.98 107	63.28 152	92.27 223	64.60 268	92.99 72	87.27 321

HPM-MVS_fast		85.35 73	84.95 79	86.57 59	93.69 42	70.58 80	92.15 36	91.62 118	73.89 151	82.67 120	94.09 50	62.60 164	95.54 66	80.93 103	92.93 73	93.57 92

SR-MVS		86.73 40	86.67 43	86.91 51	94.11 37	72.11 49	92.37 29	92.56 76	74.50 133	86.84 58	94.65 26	67.31 113	95.77 60	84.80 61	92.85 74	92.84 129

fmvsm_s_conf0.5_n_6		85.55 66	86.20 50	83.60 163	87.32 228	65.13 211	88.86 123	91.63 117	75.41 107	88.23 34	93.45 74	68.56 99	92.47 213	89.52 16	92.78 75	93.20 111

旧先验1					91.96 76	65.79 195		86.37 275			93.08 85	69.31 88			92.74 76	88.74 287

3Dnovator	76.31 5	83.38 106	82.31 118	86.59 57	87.94 200	72.94 28	90.64 63	92.14 97	77.21 62	75.47 249	92.83 90	58.56 218	94.72 110	73.24 188	92.71 77	92.13 159

MVS_111021_HR		85.14 76	84.75 81	86.32 61	91.65 81	72.70 30	85.98 227	90.33 158	76.11 94	82.08 125	91.61 121	71.36 63	94.17 130	81.02 102	92.58 78	92.08 160

APD-MVS_3200maxsize		85.97 56	85.88 59	86.22 63	92.69 68	69.53 95	91.93 38	92.99 50	73.54 161	85.94 62	94.51 30	65.80 132	95.61 63	83.04 82	92.51 79	93.53 96

test2506		77.30 248	76.49 245	79.74 274	90.08 112	52.02 392	87.86 169	63.10 435	74.88 124	80.16 155	92.79 93	38.29 399	92.35 220	68.74 233	92.50 80	94.86 19

ECVR-MVS		79.61 185	79.26 178	80.67 254	90.08 112	54.69 375	87.89 167	77.44 388	74.88 124	80.27 152	92.79 93	48.96 325	92.45 214	68.55 234	92.50 80	94.86 19

test1111		79.43 192	79.18 181	80.15 266	89.99 117	53.31 388	87.33 183	77.05 392	75.04 118	80.23 154	92.77 95	48.97 324	92.33 222	68.87 231	92.40 82	94.81 22

patch_mono-2		83.65 96	84.54 83	80.99 246	90.06 116	65.83 192	84.21 277	88.74 223	71.60 198	85.01 72	92.44 98	74.51 26	83.50 372	82.15 93	92.15 83	93.64 89

dcpmvs_2		85.63 64	86.15 54	84.06 144	91.71 80	64.94 218	86.47 213	91.87 108	73.63 157	86.60 60	93.02 86	76.57 15	91.87 239	83.36 77	92.15 83	95.35 3

fmvsm_s_conf0.5_n_9		87.39 30	87.95 20	85.70 77	89.48 133	67.88 145	88.59 139	89.05 207	80.19 12	90.70 17	95.40 15	74.56 25	93.92 142	91.54 2	92.07 85	95.31 5

MAR-MVS		81.84 131	80.70 141	85.27 89	91.32 85	71.53 58	89.82 82	90.92 138	69.77 244	78.50 179	86.21 274	62.36 170	94.52 116	65.36 261	92.05 86	89.77 250
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
TSAR-MVS + GP.		85.71 63	85.33 72	86.84 52	91.34 84	72.50 36	89.07 117	87.28 255	76.41 85	85.80 64	90.22 159	74.15 32	95.37 81	81.82 95	91.88 87	92.65 135

SR-MVS-dyc-post		85.77 61	85.61 66	86.23 62	93.06 60	70.63 78	91.88 39	92.27 85	73.53 162	85.69 66	94.45 32	65.00 140	95.56 64	82.75 86	91.87 88	92.50 141

RE-MVS-def			85.48 69		93.06 60	70.63 78	91.88 39	92.27 85	73.53 162	85.69 66	94.45 32	63.87 148		82.75 86	91.87 88	92.50 141

IS-MVSNet		83.15 111	82.81 109	84.18 134	89.94 119	63.30 257	91.59 46	88.46 229	79.04 30	79.49 162	92.16 104	65.10 137	94.28 122	67.71 240	91.86 90	94.95 12

BP-MVS1		84.32 85	83.71 94	86.17 64	87.84 205	67.85 146	89.38 102	89.64 182	77.73 45	83.98 99	92.12 106	56.89 236	95.43 73	84.03 73	91.75 91	95.24 7

fmvsm_s_conf0.5_n_3		86.36 49	87.46 29	83.09 184	87.08 236	65.21 208	89.09 116	90.21 163	79.67 19	89.98 19	95.02 20	73.17 39	91.71 245	91.30 3	91.60 92	92.34 147

Vis-MVSNet (Re-imp)		78.36 220	78.45 193	78.07 307	88.64 170	51.78 398	86.70 206	79.63 370	74.14 145	75.11 268	90.83 147	61.29 191	89.75 295	58.10 329	91.60 92	92.69 133

MG-MVS		83.41 104	83.45 97	83.28 174	92.74 67	62.28 276	88.17 155	89.50 187	75.22 112	81.49 134	92.74 96	66.75 116	95.11 90	72.85 191	91.58 94	92.45 144

CPTT-MVS		83.73 94	83.33 101	84.92 103	93.28 49	70.86 74	92.09 37	90.38 154	68.75 270	79.57 161	92.83 90	60.60 206	93.04 194	80.92 104	91.56 95	90.86 196

test222					91.50 82	68.26 133	84.16 278	83.20 323	54.63 407	79.74 158	91.63 119	58.97 216			91.42 96	86.77 335

fmvsm_s_conf0.5_n_8		86.56 44	87.17 35	84.73 110	87.76 212	65.62 199	89.20 107	92.21 90	79.94 17	89.74 22	94.86 22	68.63 98	94.20 127	90.83 5	91.39 97	94.38 45

ETV-MVS		84.90 82	84.67 82	85.59 81	89.39 138	68.66 123	88.74 133	92.64 73	79.97 16	84.10 96	85.71 283	69.32 87	95.38 78	80.82 105	91.37 98	92.72 130

testdata				79.97 269	90.90 94	64.21 234		84.71 296	59.27 378	85.40 68	92.91 87	62.02 177	89.08 309	68.95 230	91.37 98	86.63 339

API-MVS		81.99 129	81.23 133	84.26 131	90.94 93	70.18 87	91.10 58	89.32 193	71.51 200	78.66 175	88.28 212	65.26 135	95.10 93	64.74 267	91.23 100	87.51 314

casdiffmvs_mvg		85.99 54	86.09 56	85.70 77	87.65 216	67.22 169	88.69 135	93.04 42	79.64 21	85.33 69	92.54 97	73.30 36	94.50 117	83.49 76	91.14 101	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_7		83.34 107	84.03 90	81.28 237	85.73 266	65.13 211	85.40 246	89.90 173	74.96 122	82.13 124	93.89 62	66.65 117	87.92 327	86.56 47	91.05 102	90.80 197

fmvsm_s_conf0.5_n_5		85.22 75	85.55 67	84.25 132	86.26 252	67.40 161	89.18 108	89.31 194	72.50 181	88.31 31	93.86 63	69.66 83	91.96 233	89.81 11	91.05 102	93.38 99

Vis-MVSNet		83.46 103	82.80 110	85.43 85	90.25 108	68.74 117	90.30 75	90.13 166	76.33 91	80.87 144	92.89 88	61.00 197	94.20 127	72.45 198	90.97 104	93.35 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	72.83 10	79.77 183	78.33 198	84.09 140	85.17 281	69.91 89	90.57 64	90.97 137	66.70 293	72.17 312	91.91 108	54.70 253	93.96 135	61.81 294	90.95 105	88.41 296

SymmetryMVS		85.38 72	84.81 80	87.07 46	91.47 83	72.47 38	91.65 43	88.06 236	79.31 24	84.39 89	92.18 102	64.64 142	95.53 67	80.70 108	90.91 106	93.21 109

UA-Net		85.08 78	84.96 78	85.45 84	92.07 75	68.07 140	89.78 85	90.86 142	82.48 2	84.60 85	93.20 80	69.35 86	95.22 84	71.39 204	90.88 107	93.07 117

test_fmvsmconf_n		85.92 57	86.04 57	85.57 82	85.03 288	69.51 96	89.62 92	90.58 147	73.42 165	87.75 44	94.02 54	72.85 44	93.24 175	90.37 7	90.75 108	93.96 64

ACMMP		85.89 60	85.39 70	87.38 40	93.59 45	72.63 33	92.74 21	93.18 40	76.78 76	80.73 147	93.82 65	64.33 144	96.29 42	82.67 91	90.69 109	93.23 106
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
test_fmvsmconf0.1_n		85.61 65	85.65 65	85.50 83	82.99 337	69.39 103	89.65 89	90.29 161	73.31 168	87.77 43	94.15 48	71.72 56	93.23 176	90.31 8	90.67 110	93.89 70

fmvsm_l_conf0.5_n_3		86.02 52	86.32 47	85.14 92	87.20 231	68.54 126	89.57 93	90.44 152	75.31 111	87.49 48	94.39 37	72.86 43	92.72 202	89.04 24	90.56 111	94.16 54

casdiffmvs		85.11 77	85.14 76	85.01 98	87.20 231	65.77 196	87.75 170	92.83 61	77.84 43	84.36 92	92.38 99	72.15 50	93.93 141	81.27 101	90.48 112	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsm_n_1920		85.29 74	85.34 71	85.13 95	86.12 258	69.93 88	88.65 137	90.78 143	69.97 238	88.27 32	93.98 59	71.39 62	91.54 253	88.49 32	90.45 113	93.91 67

UGNet		80.83 154	79.59 169	84.54 114	88.04 195	68.09 139	89.42 99	88.16 231	76.95 70	76.22 235	89.46 180	49.30 319	93.94 138	68.48 235	90.31 114	91.60 169
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
baseline		84.93 80	84.98 77	84.80 108	87.30 229	65.39 205	87.30 184	92.88 58	77.62 47	84.04 98	92.26 101	71.81 54	93.96 135	81.31 99	90.30 115	95.03 11

MVSFormer		82.85 117	82.05 123	85.24 90	87.35 222	70.21 82	90.50 67	90.38 154	68.55 273	81.32 136	89.47 178	61.68 180	93.46 166	78.98 122	90.26 116	92.05 161

lupinMVS		81.39 144	80.27 152	84.76 109	87.35 222	70.21 82	85.55 241	86.41 273	62.85 345	81.32 136	88.61 202	61.68 180	92.24 225	78.41 129	90.26 116	91.83 164

DP-MVS Recon		83.11 114	82.09 122	86.15 66	94.44 19	70.92 73	88.79 128	92.20 91	70.53 223	79.17 166	91.03 142	64.12 146	96.03 51	68.39 237	90.14 118	91.50 174

EIA-MVS		83.31 109	82.80 110	84.82 106	89.59 126	65.59 200	88.21 153	92.68 67	74.66 131	78.96 168	86.42 270	69.06 92	95.26 83	75.54 164	90.09 119	93.62 90

MVS_111021_LR		82.61 120	82.11 120	84.11 135	88.82 161	71.58 57	85.15 250	86.16 279	74.69 129	80.47 151	91.04 140	62.29 171	90.55 283	80.33 112	90.08 120	90.20 225

jason		81.39 144	80.29 151	84.70 111	86.63 248	69.90 90	85.95 228	86.77 268	63.24 338	81.07 142	89.47 178	61.08 196	92.15 227	78.33 130	90.07 121	92.05 161
jason: jason.
test_fmvsmvis_n_1920		84.02 89	83.87 91	84.49 117	84.12 306	69.37 104	88.15 157	87.96 238	70.01 236	83.95 100	93.23 79	68.80 97	91.51 256	88.61 29	89.96 122	92.57 136

test_fmvsmconf0.01_n		84.73 83	84.52 85	85.34 87	80.25 378	69.03 106	89.47 95	89.65 181	73.24 172	86.98 56	94.27 41	66.62 118	93.23 176	90.26 9	89.95 123	93.78 79

LFMVS		81.82 132	81.23 133	83.57 166	91.89 78	63.43 255	89.84 81	81.85 342	77.04 69	83.21 110	93.10 81	52.26 276	93.43 168	71.98 199	89.95 123	93.85 71

KinetiMVS		83.31 109	82.61 113	85.39 86	87.08 236	67.56 156	88.06 159	91.65 116	77.80 44	82.21 123	91.79 113	57.27 231	94.07 133	77.77 136	89.89 125	94.56 37

MVS		78.19 225	76.99 233	81.78 223	85.66 267	66.99 172	84.66 262	90.47 151	55.08 406	72.02 314	85.27 296	63.83 149	94.11 132	66.10 255	89.80 126	84.24 376

GDP-MVS		83.52 101	82.64 112	86.16 65	88.14 189	68.45 128	89.13 114	92.69 66	72.82 180	83.71 104	91.86 112	55.69 243	95.35 82	80.03 114	89.74 127	94.69 28

CANet_DTU		80.61 165	79.87 162	82.83 198	85.60 270	63.17 262	87.36 181	88.65 225	76.37 89	75.88 242	88.44 208	53.51 265	93.07 190	73.30 186	89.74 127	92.25 152

Elysia		81.53 139	80.16 154	85.62 79	85.51 272	68.25 134	88.84 126	92.19 92	71.31 203	80.50 149	89.83 165	46.89 336	94.82 104	76.85 147	89.57 129	93.80 77

StellarMVS		81.53 139	80.16 154	85.62 79	85.51 272	68.25 134	88.84 126	92.19 92	71.31 203	80.50 149	89.83 165	46.89 336	94.82 104	76.85 147	89.57 129	93.80 77

PVSNet_Blended		80.98 150	80.34 149	82.90 195	88.85 158	65.40 203	84.43 272	92.00 100	67.62 284	78.11 190	85.05 304	66.02 129	94.27 123	71.52 201	89.50 131	89.01 272

PAPM_NR		83.02 115	82.41 115	84.82 106	92.47 72	66.37 181	87.93 165	91.80 111	73.82 152	77.32 206	90.66 149	67.90 107	94.90 100	70.37 214	89.48 132	93.19 112

114514_t		80.68 163	79.51 170	84.20 133	94.09 38	67.27 166	89.64 90	91.11 135	58.75 385	74.08 286	90.72 148	58.10 221	95.04 95	69.70 222	89.42 133	90.30 222

LCM-MVSNet-Re		77.05 250	76.94 234	77.36 320	87.20 231	51.60 399	80.06 342	80.46 358	75.20 114	67.69 358	86.72 255	62.48 167	88.98 311	63.44 275	89.25 134	91.51 173

fmvsm_l_conf0.5_n_a		84.13 87	84.16 88	84.06 144	85.38 276	68.40 129	88.34 149	86.85 267	67.48 287	87.48 49	93.40 75	70.89 68	91.61 246	88.38 34	89.22 135	92.16 158

mvsmamba		80.60 166	79.38 173	84.27 129	89.74 124	67.24 168	87.47 177	86.95 263	70.02 235	75.38 255	88.93 192	51.24 294	92.56 208	75.47 166	89.22 135	93.00 124

fmvsm_l_conf0.5_n		84.47 84	84.54 83	84.27 129	85.42 275	68.81 112	88.49 142	87.26 257	68.08 280	88.03 38	93.49 70	72.04 52	91.77 241	88.90 26	89.14 137	92.24 154

alignmvs		85.48 67	85.32 73	85.96 73	89.51 130	69.47 98	89.74 86	92.47 77	76.17 93	87.73 46	91.46 126	70.32 75	93.78 149	81.51 96	88.95 138	94.63 33

VNet		82.21 124	82.41 115	81.62 226	90.82 96	60.93 292	84.47 268	89.78 175	76.36 90	84.07 97	91.88 110	64.71 141	90.26 285	70.68 211	88.89 139	93.66 83

PS-MVSNAJ		81.69 135	81.02 137	83.70 161	89.51 130	68.21 137	84.28 276	90.09 167	70.79 215	81.26 140	85.62 288	63.15 158	94.29 121	75.62 162	88.87 140	88.59 291

sasdasda		85.91 58	85.87 61	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 39	91.23 131	73.28 37	93.91 143	81.50 97	88.80 141	94.77 25

canonicalmvs		85.91 58	85.87 61	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 39	91.23 131	73.28 37	93.91 143	81.50 97	88.80 141	94.77 25

QAPM		80.88 152	79.50 171	85.03 97	88.01 198	68.97 110	91.59 46	92.00 100	66.63 299	75.15 267	92.16 104	57.70 225	95.45 71	63.52 273	88.76 143	90.66 205

MGCFI-Net		85.06 79	85.51 68	83.70 161	89.42 135	63.01 263	89.43 97	92.62 74	76.43 84	87.53 47	91.34 129	72.82 45	93.42 169	81.28 100	88.74 144	94.66 32

VDD-MVS		83.01 116	82.36 117	84.96 100	91.02 91	66.40 180	88.91 121	88.11 232	77.57 49	84.39 89	93.29 78	52.19 277	93.91 143	77.05 145	88.70 145	94.57 36

PVSNet_Blended_VisFu		82.62 119	81.83 128	84.96 100	90.80 97	69.76 93	88.74 133	91.70 115	69.39 250	78.96 168	88.46 207	65.47 134	94.87 103	74.42 174	88.57 146	90.24 224

xiu_mvs_v2_base		81.69 135	81.05 136	83.60 163	89.15 150	68.03 142	84.46 270	90.02 168	70.67 218	81.30 139	86.53 268	63.17 157	94.19 129	75.60 163	88.54 147	88.57 292

PAPR		81.66 137	80.89 140	83.99 152	90.27 107	64.00 237	86.76 205	91.77 114	68.84 269	77.13 216	89.50 176	67.63 109	94.88 102	67.55 242	88.52 148	93.09 116

MVS_Test		83.15 111	83.06 104	83.41 171	86.86 239	63.21 259	86.11 225	92.00 100	74.31 139	82.87 115	89.44 183	70.03 78	93.21 178	77.39 141	88.50 149	93.81 75

fmvsm_s_conf0.5_n_4		85.39 71	85.75 64	84.30 125	86.70 245	65.83 192	88.77 129	89.78 175	75.46 106	88.35 30	93.73 67	69.19 89	93.06 191	91.30 3	88.44 150	94.02 62

AdaColmap		80.58 169	79.42 172	84.06 144	93.09 59	68.91 111	89.36 103	88.97 213	69.27 254	75.70 245	89.69 169	57.20 233	95.77 60	63.06 278	88.41 151	87.50 315

VDDNet		81.52 141	80.67 142	84.05 147	90.44 104	64.13 236	89.73 87	85.91 282	71.11 209	83.18 111	93.48 71	50.54 303	93.49 163	73.40 185	88.25 152	94.54 39

PCF-MVS	73.52 7	80.38 172	78.84 187	85.01 98	87.71 213	68.99 109	83.65 287	91.46 126	63.00 342	77.77 198	90.28 155	66.10 126	95.09 94	61.40 297	88.22 153	90.94 194
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
RRT-MVS		82.60 122	82.10 121	84.10 136	87.98 199	62.94 268	87.45 179	91.27 128	77.42 56	79.85 157	90.28 155	56.62 239	94.70 112	79.87 117	88.15 154	94.67 29

fmvsm_s_conf0.5_n_2		84.04 88	84.11 89	83.81 159	86.17 256	65.00 216	86.96 194	87.28 255	74.35 137	88.25 33	94.23 44	61.82 178	92.60 205	89.85 10	88.09 155	93.84 73

Effi-MVS+		83.62 99	83.08 103	85.24 90	88.38 180	67.45 158	88.89 122	89.15 203	75.50 105	82.27 121	88.28 212	69.61 84	94.45 119	77.81 135	87.84 156	93.84 73

fmvsm_s_conf0.1_n_2		83.80 92	83.79 93	83.83 157	85.62 269	64.94 218	87.03 191	86.62 271	74.32 138	87.97 41	94.33 38	60.67 202	92.60 205	89.72 12	87.79 157	93.96 64

gg-mvs-nofinetune		69.95 344	67.96 347	75.94 331	83.07 332	54.51 378	77.23 380	70.29 416	63.11 340	70.32 329	62.33 430	43.62 367	88.69 317	53.88 359	87.76 158	84.62 373

xiu_mvs_v1_base_debu		80.80 158	79.72 165	84.03 149	87.35 222	70.19 84	85.56 238	88.77 219	69.06 263	81.83 127	88.16 216	50.91 297	92.85 198	78.29 131	87.56 159	89.06 267

xiu_mvs_v1_base		80.80 158	79.72 165	84.03 149	87.35 222	70.19 84	85.56 238	88.77 219	69.06 263	81.83 127	88.16 216	50.91 297	92.85 198	78.29 131	87.56 159	89.06 267

xiu_mvs_v1_base_debi		80.80 158	79.72 165	84.03 149	87.35 222	70.19 84	85.56 238	88.77 219	69.06 263	81.83 127	88.16 216	50.91 297	92.85 198	78.29 131	87.56 159	89.06 267

CLD-MVS		82.31 123	81.65 129	84.29 126	88.47 175	67.73 150	85.81 235	92.35 83	75.78 99	78.33 185	86.58 265	64.01 147	94.35 120	76.05 157	87.48 162	90.79 198
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
myMVS_eth3d28		73.62 300	73.53 290	73.90 359	88.20 185	47.41 418	78.06 372	79.37 372	74.29 141	73.98 287	84.29 318	44.67 358	83.54 371	51.47 371	87.39 163	90.74 202

CDS-MVSNet		79.07 203	77.70 218	83.17 181	87.60 217	68.23 136	84.40 274	86.20 278	67.49 286	76.36 232	86.54 267	61.54 183	90.79 278	61.86 293	87.33 164	90.49 213
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
diffmvs		82.10 125	81.88 127	82.76 207	83.00 335	63.78 244	83.68 286	89.76 177	72.94 177	82.02 126	89.85 164	65.96 131	90.79 278	82.38 92	87.30 165	93.71 81
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPP-MVSNet		83.40 105	83.02 105	84.57 113	90.13 110	64.47 229	92.32 31	90.73 144	74.45 136	79.35 164	91.10 137	69.05 93	95.12 88	72.78 192	87.22 166	94.13 56

TAMVS		78.89 208	77.51 223	83.03 189	87.80 207	67.79 149	84.72 260	85.05 294	67.63 283	76.75 221	87.70 228	62.25 172	90.82 277	58.53 324	87.13 167	90.49 213

TAPA-MVS	73.13 9	79.15 200	77.94 206	82.79 204	89.59 126	62.99 267	88.16 156	91.51 122	65.77 308	77.14 215	91.09 138	60.91 198	93.21 178	50.26 381	87.05 168	92.17 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPM		77.68 241	76.40 248	81.51 229	87.29 230	61.85 281	83.78 283	89.59 184	64.74 321	71.23 322	88.70 198	62.59 165	93.66 156	52.66 365	87.03 169	89.01 272

test_yl		81.17 146	80.47 147	83.24 177	89.13 151	63.62 245	86.21 222	89.95 171	72.43 185	81.78 131	89.61 173	57.50 228	93.58 157	70.75 209	86.90 170	92.52 139

DCV-MVSNet		81.17 146	80.47 147	83.24 177	89.13 151	63.62 245	86.21 222	89.95 171	72.43 185	81.78 131	89.61 173	57.50 228	93.58 157	70.75 209	86.90 170	92.52 139

LuminaMVS		80.68 163	79.62 168	83.83 157	85.07 287	68.01 143	86.99 193	88.83 216	70.36 226	81.38 135	87.99 223	50.11 307	92.51 212	79.02 120	86.89 172	90.97 192

BH-untuned		79.47 190	78.60 190	82.05 218	89.19 149	65.91 190	86.07 226	88.52 228	72.18 187	75.42 253	87.69 229	61.15 194	93.54 161	60.38 305	86.83 173	86.70 337

BH-RMVSNet		79.61 185	78.44 194	83.14 182	89.38 139	65.93 189	84.95 256	87.15 260	73.56 160	78.19 188	89.79 167	56.67 238	93.36 170	59.53 313	86.74 174	90.13 228

LS3D		76.95 253	74.82 271	83.37 172	90.45 103	67.36 163	89.15 113	86.94 264	61.87 358	69.52 342	90.61 150	51.71 290	94.53 115	46.38 403	86.71 175	88.21 300

Fast-Effi-MVS+		80.81 155	79.92 160	83.47 167	88.85 158	64.51 226	85.53 243	89.39 190	70.79 215	78.49 180	85.06 303	67.54 110	93.58 157	67.03 250	86.58 176	92.32 149

EPNet_dtu		75.46 278	74.86 270	77.23 323	82.57 346	54.60 376	86.89 198	83.09 324	71.64 194	66.25 380	85.86 281	55.99 241	88.04 326	54.92 353	86.55 177	89.05 270
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		83.50 102	82.95 107	85.14 92	88.79 164	70.95 71	89.13 114	91.52 121	77.55 52	80.96 143	91.75 114	60.71 200	94.50 117	79.67 119	86.51 178	89.97 242
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
OMC-MVS		82.69 118	81.97 126	84.85 105	88.75 166	67.42 159	87.98 161	90.87 141	74.92 123	79.72 159	91.65 117	62.19 174	93.96 135	75.26 168	86.42 179	93.16 113

HQP_MVS		83.64 97	83.14 102	85.14 92	90.08 112	68.71 119	91.25 55	92.44 78	79.12 28	78.92 170	91.00 144	60.42 208	95.38 78	78.71 125	86.32 180	91.33 179

plane_prior5								92.44 78					95.38 78	78.71 125	86.32 180	91.33 179

FA-MVS(test-final)		80.96 151	79.91 161	84.10 136	88.30 183	65.01 215	84.55 267	90.01 169	73.25 171	79.61 160	87.57 232	58.35 220	94.72 110	71.29 205	86.25 182	92.56 137

thisisatest0515		77.33 247	75.38 263	83.18 180	85.27 280	63.80 243	82.11 312	83.27 319	65.06 317	75.91 241	83.84 328	49.54 314	94.27 123	67.24 246	86.19 183	91.48 176

plane_prior						68.71 119	90.38 73		77.62 47						86.16 184

UWE-MVS		72.13 322	71.49 312	74.03 357	86.66 247	47.70 415	81.40 322	76.89 394	63.60 337	75.59 246	84.22 322	39.94 389	85.62 353	48.98 388	86.13 185	88.77 284

mvs_anonymous		79.42 193	79.11 182	80.34 261	84.45 301	57.97 327	82.59 307	87.62 248	67.40 288	76.17 239	88.56 205	68.47 100	89.59 298	70.65 212	86.05 186	93.47 97

GeoE		81.71 134	81.01 138	83.80 160	89.51 130	64.45 230	88.97 119	88.73 224	71.27 206	78.63 176	89.76 168	66.32 124	93.20 181	69.89 220	86.02 187	93.74 80

HQP3-MVS								92.19 92							85.99 188

HQP-MVS		82.61 120	82.02 124	84.37 120	89.33 140	66.98 173	89.17 109	92.19 92	76.41 85	77.23 209	90.23 158	60.17 211	95.11 90	77.47 139	85.99 188	91.03 189

BH-w/o		78.21 223	77.33 227	80.84 250	88.81 162	65.13 211	84.87 257	87.85 243	69.75 245	74.52 281	84.74 310	61.34 189	93.11 188	58.24 328	85.84 190	84.27 375

FE-MVS		77.78 236	75.68 255	84.08 141	88.09 193	66.00 187	83.13 300	87.79 244	68.42 277	78.01 193	85.23 298	45.50 355	95.12 88	59.11 317	85.83 191	91.11 185

testing222		74.04 295	72.66 301	78.19 304	87.89 202	55.36 368	81.06 325	79.20 375	71.30 205	74.65 279	83.57 338	39.11 394	88.67 318	51.43 373	85.75 192	90.53 211

CHOSEN 1792x2688		77.63 242	75.69 254	83.44 168	89.98 118	68.58 125	78.70 362	87.50 251	56.38 401	75.80 244	86.84 251	58.67 217	91.40 261	61.58 296	85.75 192	90.34 219

ICG_test		80.80 158	80.12 157	82.87 197	87.13 234	63.59 249	85.19 247	89.33 192	70.51 224	78.49 180	89.03 190	63.26 154	93.27 173	72.56 197	85.56 194	91.74 167

guyue		81.13 148	80.64 143	82.60 210	86.52 249	63.92 241	86.69 207	87.73 246	73.97 147	80.83 146	89.69 169	56.70 237	91.33 264	78.26 134	85.40 195	92.54 138

Anonymous202405211		78.25 221	77.01 231	81.99 220	91.03 90	60.67 297	84.77 259	83.90 309	70.65 222	80.00 156	91.20 134	41.08 384	91.43 260	65.21 262	85.26 196	93.85 71

cascas		76.72 257	74.64 273	82.99 191	85.78 265	65.88 191	82.33 309	89.21 200	60.85 364	72.74 302	81.02 370	47.28 332	93.75 153	67.48 243	85.02 197	89.34 262

FIs		82.07 127	82.42 114	81.04 245	88.80 163	58.34 321	88.26 152	93.49 27	76.93 71	78.47 182	91.04 140	69.92 80	92.34 221	69.87 221	84.97 198	92.44 145

test-LLR		72.94 314	72.43 303	74.48 351	81.35 366	58.04 325	78.38 366	77.46 386	66.66 294	69.95 337	79.00 393	48.06 328	79.24 393	66.13 253	84.83 199	86.15 345

test-mter		71.41 326	70.39 328	74.48 351	81.35 366	58.04 325	78.38 366	77.46 386	60.32 368	69.95 337	79.00 393	36.08 408	79.24 393	66.13 253	84.83 199	86.15 345

EI-MVSNet-Vis-set		84.19 86	83.81 92	85.31 88	88.18 186	67.85 146	87.66 172	89.73 179	80.05 15	82.95 113	89.59 175	70.74 71	94.82 104	80.66 110	84.72 201	93.28 105

thisisatest0530		79.40 194	77.76 216	84.31 124	87.69 215	65.10 214	87.36 181	84.26 305	70.04 234	77.42 203	88.26 214	49.94 310	94.79 108	70.20 215	84.70 202	93.03 121

fmvsm_s_conf0.5_n		83.80 92	83.71 94	84.07 142	86.69 246	67.31 164	89.46 96	83.07 325	71.09 210	86.96 57	93.70 68	69.02 95	91.47 258	88.79 27	84.62 203	93.44 98

testing91		76.54 258	75.66 257	79.18 286	88.43 178	55.89 361	81.08 324	83.00 327	73.76 154	75.34 257	84.29 318	46.20 346	90.07 289	64.33 269	84.50 204	91.58 171

fmvsm_s_conf0.1_n		83.56 100	83.38 99	84.10 136	84.86 290	67.28 165	89.40 101	83.01 326	70.67 218	87.08 54	93.96 60	68.38 101	91.45 259	88.56 31	84.50 204	93.56 93

GG-mvs-BLEND				75.38 341	81.59 360	55.80 363	79.32 351	69.63 418		67.19 365	73.67 419	43.24 369	88.90 315	50.41 376	84.50 204	81.45 404

FC-MVSNet-test		81.52 141	82.02 124	80.03 268	88.42 179	55.97 360	87.95 163	93.42 30	77.10 67	77.38 204	90.98 146	69.96 79	91.79 240	68.46 236	84.50 204	92.33 148

PVSNet	64.34 18	72.08 323	70.87 322	75.69 334	86.21 254	56.44 352	74.37 399	80.73 353	62.06 356	70.17 332	82.23 361	42.86 372	83.31 374	54.77 354	84.45 208	87.32 319

ETVMVS		72.25 320	71.05 319	75.84 332	87.77 211	51.91 395	79.39 350	74.98 401	69.26 255	73.71 290	82.95 348	40.82 386	86.14 346	46.17 404	84.43 209	89.47 257

UBG		73.08 311	72.27 306	75.51 338	88.02 196	51.29 403	78.35 369	77.38 389	65.52 312	73.87 289	82.36 357	45.55 353	86.48 343	55.02 352	84.39 210	88.75 285

MS-PatchMatch		73.83 298	72.67 300	77.30 322	83.87 313	66.02 186	81.82 313	84.66 297	61.37 362	68.61 351	82.82 352	47.29 331	88.21 323	59.27 314	84.32 211	77.68 417

ET-MVSNet_ETH3D		78.63 213	76.63 244	84.64 112	86.73 244	69.47 98	85.01 254	84.61 298	69.54 248	66.51 378	86.59 263	50.16 306	91.75 242	76.26 154	84.24 212	92.69 133

testing99		76.09 270	75.12 269	79.00 287	88.16 187	55.50 367	80.79 328	81.40 347	73.30 169	75.17 265	84.27 321	44.48 361	90.02 290	64.28 270	84.22 213	91.48 176

TESTMET0.1,1		69.89 345	69.00 337	72.55 371	79.27 394	56.85 344	78.38 366	74.71 405	57.64 393	68.09 355	77.19 406	37.75 401	76.70 406	63.92 272	84.09 214	84.10 379

AstraMVS		80.81 155	80.14 156	82.80 201	86.05 261	63.96 238	86.46 214	85.90 283	73.71 155	80.85 145	90.56 151	54.06 260	91.57 250	79.72 118	83.97 215	92.86 128

EI-MVSNet-UG-set		83.81 91	83.38 99	85.09 96	87.87 203	67.53 157	87.44 180	89.66 180	79.74 18	82.23 122	89.41 184	70.24 77	94.74 109	79.95 115	83.92 216	92.99 125

LPG-MVS_test		82.08 126	81.27 132	84.50 115	89.23 147	68.76 115	90.22 76	91.94 104	75.37 109	76.64 224	91.51 123	54.29 256	94.91 98	78.44 127	83.78 217	89.83 247

LGP-MVS_train				84.50 115	89.23 147	68.76 115		91.94 104	75.37 109	76.64 224	91.51 123	54.29 256	94.91 98	78.44 127	83.78 217	89.83 247

testing11		75.14 284	74.01 282	78.53 298	88.16 187	56.38 354	80.74 331	80.42 360	70.67 218	72.69 305	83.72 333	43.61 368	89.86 292	62.29 287	83.76 219	89.36 261

thres100view900		76.50 260	75.55 259	79.33 282	89.52 129	56.99 343	85.83 234	83.23 320	73.94 149	76.32 233	87.12 247	51.89 286	91.95 234	48.33 391	83.75 220	89.07 265

tfpn200view9		76.42 264	75.37 264	79.55 281	89.13 151	57.65 334	85.17 248	83.60 312	73.41 166	76.45 229	86.39 271	52.12 278	91.95 234	48.33 391	83.75 220	89.07 265

thres400		76.50 260	75.37 264	79.86 271	89.13 151	57.65 334	85.17 248	83.60 312	73.41 166	76.45 229	86.39 271	52.12 278	91.95 234	48.33 391	83.75 220	90.00 238

thres600view7		76.50 260	75.44 260	79.68 276	89.40 137	57.16 340	85.53 243	83.23 320	73.79 153	76.26 234	87.09 248	51.89 286	91.89 237	48.05 396	83.72 223	90.00 238

fmvsm_s_conf0.5_n_a		83.63 98	83.41 98	84.28 127	86.14 257	68.12 138	89.43 97	82.87 330	70.27 231	87.27 53	93.80 66	69.09 90	91.58 248	88.21 35	83.65 224	93.14 115

thres200		75.55 276	74.47 277	78.82 290	87.78 210	57.85 330	83.07 303	83.51 315	72.44 184	75.84 243	84.42 313	52.08 281	91.75 242	47.41 398	83.64 225	86.86 333

SDMVSNet		80.38 172	80.18 153	80.99 246	89.03 156	64.94 218	80.45 337	89.40 189	75.19 115	76.61 226	89.98 161	60.61 205	87.69 331	76.83 150	83.55 226	90.33 220

sd_testset		77.70 240	77.40 224	78.60 294	89.03 156	60.02 306	79.00 357	85.83 284	75.19 115	76.61 226	89.98 161	54.81 248	85.46 356	62.63 284	83.55 226	90.33 220

testing3-2		75.12 285	75.19 267	74.91 346	90.40 105	45.09 428	80.29 340	78.42 380	78.37 40	76.54 228	87.75 226	44.36 362	87.28 336	57.04 339	83.49 228	92.37 146

XVG-OURS		80.41 171	79.23 179	83.97 153	85.64 268	69.02 108	83.03 305	90.39 153	71.09 210	77.63 200	91.49 125	54.62 255	91.35 262	75.71 160	83.47 229	91.54 172

fmvsm_s_conf0.1_n_a		83.32 108	82.99 106	84.28 127	83.79 314	68.07 140	89.34 104	82.85 331	69.80 242	87.36 52	94.06 52	68.34 102	91.56 251	87.95 36	83.46 230	93.21 109

SD_0403		74.65 288	74.77 272	74.29 354	86.20 255	47.42 417	83.71 285	85.12 291	69.30 253	68.50 353	87.95 224	59.40 213	86.05 347	49.38 385	83.35 231	89.40 259

CNLPA		78.08 227	76.79 238	81.97 221	90.40 105	71.07 67	87.59 174	84.55 299	66.03 306	72.38 309	89.64 172	57.56 227	86.04 348	59.61 312	83.35 231	88.79 283

MVP-Stereo		76.12 268	74.46 278	81.13 243	85.37 277	69.79 91	84.42 273	87.95 239	65.03 318	67.46 361	85.33 295	53.28 268	91.73 244	58.01 330	83.27 233	81.85 402
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		76.53 259	75.30 266	80.21 265	83.93 311	62.32 275	84.66 262	88.81 217	60.23 369	70.16 333	84.07 325	55.30 246	90.73 281	67.37 244	83.21 234	87.59 313

tttt0517		79.40 194	77.91 207	83.90 156	88.10 192	63.84 242	88.37 148	84.05 307	71.45 201	76.78 220	89.12 187	49.93 312	94.89 101	70.18 216	83.18 235	92.96 126

HyFIR lowres test		77.53 243	75.40 262	83.94 155	89.59 126	66.62 177	80.36 338	88.64 226	56.29 402	76.45 229	85.17 300	57.64 226	93.28 172	61.34 299	83.10 236	91.91 163

ACMP	74.13 6	81.51 143	80.57 144	84.36 121	89.42 135	68.69 122	89.97 80	91.50 125	74.46 135	75.04 271	90.41 154	53.82 262	94.54 114	77.56 138	82.91 237	89.86 246
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	73.20 8	80.78 162	79.84 163	83.58 165	89.31 143	68.37 130	89.99 79	91.60 119	70.28 230	77.25 207	89.66 171	53.37 267	93.53 162	74.24 177	82.85 238	88.85 280
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PMMVS		69.34 349	68.67 338	71.35 381	75.67 408	62.03 278	75.17 391	73.46 408	50.00 419	68.68 349	79.05 391	52.07 282	78.13 398	61.16 300	82.77 239	73.90 423

PLC	70.83 11	78.05 229	76.37 249	83.08 186	91.88 79	67.80 148	88.19 154	89.46 188	64.33 327	69.87 339	88.38 209	53.66 263	93.58 157	58.86 320	82.73 240	87.86 306
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		77.44 244	76.18 250	81.20 240	88.24 184	63.24 258	84.61 265	86.40 274	67.55 285	77.81 196	86.48 269	54.10 258	93.15 185	57.75 332	82.72 241	87.20 322

Anonymous20240529		80.19 178	78.89 186	84.10 136	90.60 100	64.75 223	88.95 120	90.90 139	65.97 307	80.59 148	91.17 136	49.97 309	93.73 155	69.16 228	82.70 242	93.81 75

ab-mvs		79.51 188	78.97 185	81.14 242	88.46 176	60.91 293	83.84 282	89.24 199	70.36 226	79.03 167	88.87 195	63.23 156	90.21 287	65.12 263	82.57 243	92.28 151

HY-MVS	69.67 12	77.95 232	77.15 229	80.36 260	87.57 221	60.21 305	83.37 295	87.78 245	66.11 303	75.37 256	87.06 250	63.27 153	90.48 284	61.38 298	82.43 244	90.40 217

PS-MVSNAJss		82.07 127	81.31 131	84.34 123	86.51 250	67.27 166	89.27 105	91.51 122	71.75 193	79.37 163	90.22 159	63.15 158	94.27 123	77.69 137	82.36 245	91.49 175

UniMVSNet_ETH3D		79.10 202	78.24 200	81.70 225	86.85 240	60.24 304	87.28 185	88.79 218	74.25 142	76.84 217	90.53 153	49.48 315	91.56 251	67.98 238	82.15 246	93.29 104

WB-MVSnew		71.96 324	71.65 311	72.89 368	84.67 298	51.88 396	82.29 310	77.57 385	62.31 352	73.67 292	83.00 347	53.49 266	81.10 387	45.75 407	82.13 247	85.70 355

PVSNet_BlendedMVS		80.60 166	80.02 158	82.36 215	88.85 158	65.40 203	86.16 224	92.00 100	69.34 252	78.11 190	86.09 278	66.02 129	94.27 123	71.52 201	82.06 248	87.39 316

WTY-MVS		75.65 275	75.68 255	75.57 336	86.40 251	56.82 345	77.92 375	82.40 335	65.10 316	76.18 237	87.72 227	63.13 161	80.90 388	60.31 306	81.96 249	89.00 274

ACMMP++_ref															81.95 250

DP-MVS		76.78 256	74.57 274	83.42 169	93.29 48	69.46 100	88.55 141	83.70 311	63.98 334	70.20 330	88.89 194	54.01 261	94.80 107	46.66 400	81.88 251	86.01 349

CMPMVS	51.72 21	70.19 341	68.16 343	76.28 329	73.15 424	57.55 336	79.47 349	83.92 308	48.02 422	56.48 422	84.81 308	43.13 370	86.42 344	62.67 283	81.81 252	84.89 369
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
XVG-OURS-SEG-HR		80.81 155	79.76 164	83.96 154	85.60 270	68.78 114	83.54 293	90.50 150	70.66 221	76.71 222	91.66 116	60.69 201	91.26 265	76.94 146	81.58 253	91.83 164

MIMVSNet		70.69 334	69.30 333	74.88 347	84.52 299	56.35 356	75.87 387	79.42 371	64.59 322	67.76 356	82.41 356	41.10 383	81.54 384	46.64 402	81.34 254	86.75 336

ACMMP++															81.25 255

D2MVS		74.82 286	73.21 294	79.64 278	79.81 385	62.56 272	80.34 339	87.35 254	64.37 326	68.86 348	82.66 354	46.37 342	90.10 288	67.91 239	81.24 256	86.25 342

test_vis1_n_1920		75.52 277	75.78 253	74.75 350	79.84 384	57.44 338	83.26 297	85.52 287	62.83 346	79.34 165	86.17 276	45.10 357	79.71 392	78.75 124	81.21 257	87.10 329

GA-MVS		76.87 254	75.17 268	81.97 221	82.75 341	62.58 271	81.44 321	86.35 276	72.16 189	74.74 276	82.89 350	46.20 346	92.02 231	68.85 232	81.09 258	91.30 181

sss		73.60 301	73.64 289	73.51 362	82.80 340	55.01 373	76.12 383	81.69 343	62.47 351	74.68 278	85.85 282	57.32 230	78.11 399	60.86 302	80.93 259	87.39 316

UWE-MVS-28		65.32 376	64.93 370	66.49 404	78.70 396	38.55 441	77.86 376	64.39 433	62.00 357	64.13 393	83.60 336	41.44 381	76.00 414	31.39 433	80.89 260	84.92 368

Effi-MVS+-dtu		80.03 180	78.57 191	84.42 119	85.13 285	68.74 117	88.77 129	88.10 233	74.99 119	74.97 273	83.49 339	57.27 231	93.36 170	73.53 182	80.88 261	91.18 183

EG-PatchMatch MVS		74.04 295	71.82 309	80.71 253	84.92 289	67.42 159	85.86 232	88.08 234	66.04 305	64.22 392	83.85 327	35.10 410	92.56 208	57.44 334	80.83 262	82.16 401

jajsoiax		79.29 197	77.96 205	83.27 175	84.68 295	66.57 179	89.25 106	90.16 165	69.20 259	75.46 251	89.49 177	45.75 352	93.13 187	76.84 149	80.80 263	90.11 230

1112_ss		77.40 246	76.43 247	80.32 262	89.11 155	60.41 302	83.65 287	87.72 247	62.13 355	73.05 299	86.72 255	62.58 166	89.97 291	62.11 291	80.80 263	90.59 209

mvs_tets		79.13 201	77.77 215	83.22 179	84.70 294	66.37 181	89.17 109	90.19 164	69.38 251	75.40 254	89.46 180	44.17 364	93.15 185	76.78 151	80.70 265	90.14 227

PatchMatch-RL		72.38 317	70.90 321	76.80 327	88.60 171	67.38 162	79.53 348	76.17 398	62.75 348	69.36 344	82.00 365	45.51 354	84.89 362	53.62 360	80.58 266	78.12 416

EI-MVSNet		80.52 170	79.98 159	82.12 216	84.28 302	63.19 261	86.41 215	88.95 214	74.18 144	78.69 173	87.54 235	66.62 118	92.43 215	72.57 195	80.57 267	90.74 202

MVSTER		79.01 204	77.88 210	82.38 214	83.07 332	64.80 222	84.08 281	88.95 214	69.01 266	78.69 173	87.17 246	54.70 253	92.43 215	74.69 171	80.57 267	89.89 245

XVG-ACMP-BASELINE		76.11 269	74.27 281	81.62 226	83.20 328	64.67 224	83.60 290	89.75 178	69.75 245	71.85 315	87.09 248	32.78 414	92.11 228	69.99 219	80.43 269	88.09 302

Fast-Effi-MVS+-dtu		78.02 230	76.49 245	82.62 209	83.16 331	66.96 175	86.94 196	87.45 253	72.45 182	71.49 320	84.17 323	54.79 252	91.58 248	67.61 241	80.31 270	89.30 263

LTVRE_ROB	69.57 13	76.25 267	74.54 276	81.41 232	88.60 171	64.38 232	79.24 352	89.12 206	70.76 217	69.79 341	87.86 225	49.09 322	93.20 181	56.21 348	80.16 271	86.65 338
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
Test_1112_low_res		76.40 265	75.44 260	79.27 283	89.28 145	58.09 323	81.69 316	87.07 261	59.53 376	72.48 307	86.67 260	61.30 190	89.33 302	60.81 303	80.15 272	90.41 216

test_djsdf		80.30 175	79.32 176	83.27 175	83.98 310	65.37 206	90.50 67	90.38 154	68.55 273	76.19 236	88.70 198	56.44 240	93.46 166	78.98 122	80.14 273	90.97 192

test_fmvs1		70.93 331	70.52 324	72.16 374	73.71 417	55.05 372	80.82 326	78.77 378	51.21 418	78.58 177	84.41 314	31.20 419	76.94 405	75.88 159	80.12 274	84.47 374

test_fmvs1_n		70.86 332	70.24 329	72.73 370	72.51 428	55.28 370	81.27 323	79.71 369	51.49 417	78.73 172	84.87 306	27.54 424	77.02 404	76.06 156	79.97 275	85.88 353

CHOSEN 280x420		66.51 370	64.71 372	71.90 375	81.45 363	63.52 251	57.98 439	68.95 422	53.57 409	62.59 402	76.70 407	46.22 345	75.29 422	55.25 350	79.68 276	76.88 419

baseline2		75.70 274	73.83 287	81.30 236	83.26 326	61.79 283	82.57 308	80.65 354	66.81 290	66.88 369	83.42 340	57.86 224	92.19 226	63.47 274	79.57 277	89.91 243

GBi-Net		78.40 218	77.40 224	81.40 233	87.60 217	63.01 263	88.39 145	89.28 195	71.63 195	75.34 257	87.28 239	54.80 249	91.11 268	62.72 280	79.57 277	90.09 232

test1		78.40 218	77.40 224	81.40 233	87.60 217	63.01 263	88.39 145	89.28 195	71.63 195	75.34 257	87.28 239	54.80 249	91.11 268	62.72 280	79.57 277	90.09 232

FMVSNet3		77.88 234	76.85 236	80.97 248	86.84 241	62.36 273	86.52 212	88.77 219	71.13 208	75.34 257	86.66 261	54.07 259	91.10 271	62.72 280	79.57 277	89.45 258

FMVSNet2		78.20 224	77.21 228	81.20 240	87.60 217	62.89 269	87.47 177	89.02 209	71.63 195	75.29 263	87.28 239	54.80 249	91.10 271	62.38 285	79.38 281	89.61 254

anonymousdsp		78.60 214	77.15 229	82.98 192	80.51 376	67.08 171	87.24 186	89.53 186	65.66 310	75.16 266	87.19 245	52.52 271	92.25 224	77.17 143	79.34 282	89.61 254

nrg030		83.88 90	83.53 96	84.96 100	86.77 243	69.28 105	90.46 70	92.67 68	74.79 127	82.95 113	91.33 130	72.70 46	93.09 189	80.79 107	79.28 283	92.50 141

VPA-MVSNet		80.60 166	80.55 145	80.76 252	88.07 194	60.80 295	86.86 199	91.58 120	75.67 103	80.24 153	89.45 182	63.34 151	90.25 286	70.51 213	79.22 284	91.23 182

tt0805		78.73 210	77.83 211	81.43 231	85.17 281	60.30 303	89.41 100	90.90 139	71.21 207	77.17 214	88.73 197	46.38 341	93.21 178	72.57 195	78.96 285	90.79 198

test_cas_vis1_n_1920		73.76 299	73.74 288	73.81 360	75.90 406	59.77 308	80.51 335	82.40 335	58.30 387	81.62 133	85.69 284	44.35 363	76.41 410	76.29 153	78.61 286	85.23 362

F-COLMAP		76.38 266	74.33 280	82.50 212	89.28 145	66.95 176	88.41 144	89.03 208	64.05 332	66.83 370	88.61 202	46.78 338	92.89 197	57.48 333	78.55 287	87.67 309

FMVSNet1		77.44 244	76.12 251	81.40 233	86.81 242	63.01 263	88.39 145	89.28 195	70.49 225	74.39 283	87.28 239	49.06 323	91.11 268	60.91 301	78.52 288	90.09 232

MDTV_nov1_ep13			69.97 331		83.18 329	53.48 385	77.10 381	80.18 366	60.45 366	69.33 345	80.44 376	48.89 326	86.90 338	51.60 370	78.51 289

CVMVSNet		72.99 313	72.58 302	74.25 355	84.28 302	50.85 406	86.41 215	83.45 317	44.56 426	73.23 297	87.54 235	49.38 317	85.70 351	65.90 257	78.44 290	86.19 344

tpm2		73.26 308	71.46 313	78.63 292	83.34 324	56.71 348	80.65 333	80.40 361	56.63 400	73.55 293	82.02 364	51.80 288	91.24 266	56.35 347	78.42 291	87.95 303

test_vis1_n		69.85 346	69.21 335	71.77 376	72.66 427	55.27 371	81.48 319	76.21 397	52.03 414	75.30 262	83.20 344	28.97 422	76.22 412	74.60 172	78.41 292	83.81 382

CostFormer		75.24 283	73.90 285	79.27 283	82.65 345	58.27 322	80.80 327	82.73 333	61.57 359	75.33 261	83.13 345	55.52 244	91.07 274	64.98 265	78.34 293	88.45 294

ACMH	67.68 16	75.89 272	73.93 284	81.77 224	88.71 168	66.61 178	88.62 138	89.01 210	69.81 241	66.78 371	86.70 259	41.95 380	91.51 256	55.64 349	78.14 294	87.17 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mamv4		76.81 255	78.23 202	72.54 372	86.12 258	65.75 197	78.76 361	82.07 339	64.12 329	72.97 300	91.02 143	67.97 105	68.08 437	83.04 82	78.02 295	83.80 383

WBMVS		73.43 303	72.81 299	75.28 342	87.91 201	50.99 405	78.59 365	81.31 349	65.51 314	74.47 282	84.83 307	46.39 340	86.68 340	58.41 325	77.86 296	88.17 301

dmvs_re		71.14 328	70.58 323	72.80 369	81.96 354	59.68 309	75.60 389	79.34 373	68.55 273	69.27 346	80.72 375	49.42 316	76.54 407	52.56 366	77.79 297	82.19 400

CR-MVSNet		73.37 304	71.27 317	79.67 277	81.32 368	65.19 209	75.92 385	80.30 362	59.92 372	72.73 303	81.19 367	52.50 272	86.69 339	59.84 309	77.71 298	87.11 327

RPMNet		73.51 302	70.49 325	82.58 211	81.32 368	65.19 209	75.92 385	92.27 85	57.60 394	72.73 303	76.45 409	52.30 275	95.43 73	48.14 395	77.71 298	87.11 327

SSC-MVS3.2		73.35 307	73.39 291	73.23 363	85.30 279	49.01 413	74.58 398	81.57 344	75.21 113	73.68 291	85.58 289	52.53 270	82.05 381	54.33 357	77.69 300	88.63 290

SCA		74.22 292	72.33 305	79.91 270	84.05 309	62.17 277	79.96 345	79.29 374	66.30 302	72.38 309	80.13 382	51.95 284	88.60 319	59.25 315	77.67 301	88.96 276

Anonymous20231211		78.97 206	77.69 219	82.81 200	90.54 102	64.29 233	90.11 78	91.51 122	65.01 319	76.16 240	88.13 221	50.56 302	93.03 195	69.68 223	77.56 302	91.11 185

v1144		80.03 180	79.03 183	83.01 190	83.78 315	64.51 226	87.11 189	90.57 149	71.96 192	78.08 192	86.20 275	61.41 187	93.94 138	74.93 170	77.23 303	90.60 208

WR-MVS		79.49 189	79.22 180	80.27 263	88.79 164	58.35 320	85.06 253	88.61 227	78.56 35	77.65 199	88.34 210	63.81 150	90.66 282	64.98 265	77.22 304	91.80 166

v1192		79.59 187	78.43 195	83.07 187	83.55 320	64.52 225	86.93 197	90.58 147	70.83 214	77.78 197	85.90 279	59.15 215	93.94 138	73.96 179	77.19 305	90.76 200

VPNet		78.69 212	78.66 189	78.76 291	88.31 182	55.72 364	84.45 271	86.63 270	76.79 75	78.26 186	90.55 152	59.30 214	89.70 297	66.63 251	77.05 306	90.88 195

v1240		78.99 205	77.78 214	82.64 208	83.21 327	63.54 250	86.62 209	90.30 160	69.74 247	77.33 205	85.68 285	57.04 234	93.76 152	73.13 189	76.92 307	90.62 206

MSDG		73.36 306	70.99 320	80.49 258	84.51 300	65.80 194	80.71 332	86.13 280	65.70 309	65.46 383	83.74 331	44.60 359	90.91 276	51.13 374	76.89 308	84.74 371

IterMVS-LS		80.06 179	79.38 173	82.11 217	85.89 262	63.20 260	86.79 202	89.34 191	74.19 143	75.45 252	86.72 255	66.62 118	92.39 217	72.58 194	76.86 309	90.75 201
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		79.22 198	78.03 204	82.80 201	83.30 325	63.94 240	86.80 201	90.33 158	69.91 240	77.48 202	85.53 290	58.44 219	93.75 153	73.60 181	76.85 310	90.71 204

XXY-MVS		75.41 280	75.56 258	74.96 345	83.59 319	57.82 331	80.59 334	83.87 310	66.54 300	74.93 274	88.31 211	63.24 155	80.09 391	62.16 289	76.85 310	86.97 331

v2v482		80.23 176	79.29 177	83.05 188	83.62 318	64.14 235	87.04 190	89.97 170	73.61 158	78.18 189	87.22 243	61.10 195	93.82 147	76.11 155	76.78 312	91.18 183

VortexMVS		78.57 216	77.89 209	80.59 255	85.89 262	62.76 270	85.61 236	89.62 183	72.06 190	74.99 272	85.38 294	55.94 242	90.77 280	74.99 169	76.58 313	88.23 298

v144192		79.47 190	78.37 196	82.78 205	83.35 323	63.96 238	86.96 194	90.36 157	69.99 237	77.50 201	85.67 286	60.66 203	93.77 151	74.27 176	76.58 313	90.62 206

UniMVSNet (Re)		81.60 138	81.11 135	83.09 184	88.38 180	64.41 231	87.60 173	93.02 46	78.42 37	78.56 178	88.16 216	69.78 81	93.26 174	69.58 224	76.49 315	91.60 169

UniMVSNet_NR-MVSNet		81.88 130	81.54 130	82.92 194	88.46 176	63.46 253	87.13 187	92.37 82	80.19 12	78.38 183	89.14 186	71.66 59	93.05 192	70.05 217	76.46 316	92.25 152

DU-MVS		81.12 149	80.52 146	82.90 195	87.80 207	63.46 253	87.02 192	91.87 108	79.01 31	78.38 183	89.07 188	65.02 138	93.05 192	70.05 217	76.46 316	92.20 155

cl22		78.07 228	77.01 231	81.23 239	82.37 351	61.83 282	83.55 291	87.98 237	68.96 267	75.06 270	83.87 326	61.40 188	91.88 238	73.53 182	76.39 318	89.98 241

miper_ehance_all_eth		78.59 215	77.76 216	81.08 244	82.66 344	61.56 285	83.65 287	89.15 203	68.87 268	75.55 248	83.79 330	66.49 121	92.03 230	73.25 187	76.39 318	89.64 253

miper_enhance_ethall		77.87 235	76.86 235	80.92 249	81.65 358	61.38 287	82.68 306	88.98 211	65.52 312	75.47 249	82.30 359	65.76 133	92.00 232	72.95 190	76.39 318	89.39 260

Syy-MVS		68.05 360	67.85 349	68.67 396	84.68 295	40.97 439	78.62 363	73.08 410	66.65 297	66.74 372	79.46 388	52.11 280	82.30 379	32.89 431	76.38 321	82.75 395

myMVS_eth3d		67.02 366	66.29 367	69.21 391	84.68 295	42.58 434	78.62 363	73.08 410	66.65 297	66.74 372	79.46 388	31.53 418	82.30 379	39.43 423	76.38 321	82.75 395

PatchmatchNet		73.12 310	71.33 316	78.49 300	83.18 329	60.85 294	79.63 347	78.57 379	64.13 328	71.73 316	79.81 387	51.20 295	85.97 349	57.40 335	76.36 323	88.66 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
USDC		70.33 339	68.37 340	76.21 330	80.60 374	56.23 357	79.19 354	86.49 272	60.89 363	61.29 405	85.47 292	31.78 417	89.47 301	53.37 362	76.21 324	82.94 394

OpenMVS_ROB	64.09 19	70.56 336	68.19 342	77.65 315	80.26 377	59.41 314	85.01 254	82.96 329	58.76 384	65.43 384	82.33 358	37.63 402	91.23 267	45.34 410	76.03 325	82.32 398

ACMH+	68.96 14	76.01 271	74.01 282	82.03 219	88.60 171	65.31 207	88.86 123	87.55 249	70.25 232	67.75 357	87.47 237	41.27 382	93.19 183	58.37 326	75.94 326	87.60 311

tpm		72.37 318	71.71 310	74.35 353	82.19 352	52.00 393	79.22 353	77.29 390	64.56 323	72.95 301	83.68 335	51.35 292	83.26 375	58.33 327	75.80 327	87.81 307

Anonymous20231206		68.60 354	67.80 352	71.02 384	80.23 379	50.75 407	78.30 370	80.47 357	56.79 399	66.11 381	82.63 355	46.35 343	78.95 395	43.62 413	75.70 328	83.36 387

v7n		78.97 206	77.58 222	83.14 182	83.45 322	65.51 201	88.32 150	91.21 130	73.69 156	72.41 308	86.32 273	57.93 222	93.81 148	69.18 227	75.65 329	90.11 230

NR-MVSNet		80.23 176	79.38 173	82.78 205	87.80 207	63.34 256	86.31 219	91.09 136	79.01 31	72.17 312	89.07 188	67.20 114	92.81 201	66.08 256	75.65 329	92.20 155

v10		79.74 184	78.67 188	82.97 193	84.06 308	64.95 217	87.88 168	90.62 146	73.11 173	75.11 268	86.56 266	61.46 186	94.05 134	73.68 180	75.55 331	89.90 244

IB-MVS	68.01 15	75.85 273	73.36 293	83.31 173	84.76 293	66.03 185	83.38 294	85.06 293	70.21 233	69.40 343	81.05 369	45.76 351	94.66 113	65.10 264	75.49 332	89.25 264
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
h-mvs33		83.15 111	82.19 119	86.02 72	90.56 101	70.85 75	88.15 157	89.16 202	76.02 96	84.67 80	91.39 128	61.54 183	95.50 69	82.71 88	75.48 333	91.72 168

c3_l		78.75 209	77.91 207	81.26 238	82.89 339	61.56 285	84.09 280	89.13 205	69.97 238	75.56 247	84.29 318	66.36 123	92.09 229	73.47 184	75.48 333	90.12 229

V42		79.38 196	78.24 200	82.83 198	81.10 370	65.50 202	85.55 241	89.82 174	71.57 199	78.21 187	86.12 277	60.66 203	93.18 184	75.64 161	75.46 335	89.81 249

testing3		68.56 356	67.67 355	71.22 383	87.33 227	42.87 433	83.06 304	71.54 413	70.36 226	69.08 347	84.38 315	30.33 421	85.69 352	37.50 426	75.45 336	85.09 367

cl____		77.72 238	76.76 239	80.58 256	82.49 348	60.48 300	83.09 301	87.87 241	69.22 257	74.38 284	85.22 299	62.10 175	91.53 254	71.09 206	75.41 337	89.73 252

DIV-MVS_self_test		77.72 238	76.76 239	80.58 256	82.48 349	60.48 300	83.09 301	87.86 242	69.22 257	74.38 284	85.24 297	62.10 175	91.53 254	71.09 206	75.40 338	89.74 251

v8		79.97 182	79.02 184	82.80 201	84.09 307	64.50 228	87.96 162	90.29 161	74.13 146	75.24 264	86.81 252	62.88 163	93.89 146	74.39 175	75.40 338	90.00 238

Baseline_NR-MVSNet		78.15 226	78.33 198	77.61 316	85.79 264	56.21 358	86.78 203	85.76 285	73.60 159	77.93 195	87.57 232	65.02 138	88.99 310	67.14 248	75.33 340	87.63 310

pmmvs5		71.55 325	70.20 330	75.61 335	77.83 399	56.39 353	81.74 315	80.89 350	57.76 392	67.46 361	84.49 311	49.26 320	85.32 358	57.08 338	75.29 341	85.11 366

EPMVS		69.02 351	68.16 343	71.59 377	79.61 389	49.80 412	77.40 378	66.93 426	62.82 347	70.01 334	79.05 391	45.79 350	77.86 401	56.58 345	75.26 342	87.13 326

TranMVSNet+NR-MVSNet		80.84 153	80.31 150	82.42 213	87.85 204	62.33 274	87.74 171	91.33 127	80.55 9	77.99 194	89.86 163	65.23 136	92.62 203	67.05 249	75.24 343	92.30 150

test_fmvs2		68.35 359	67.48 358	70.98 385	69.50 431	51.95 394	80.05 343	76.38 396	49.33 420	74.65 279	84.38 315	23.30 433	75.40 421	74.51 173	75.17 344	85.60 356

tfpnnormal		74.39 289	73.16 295	78.08 306	86.10 260	58.05 324	84.65 264	87.53 250	70.32 229	71.22 323	85.63 287	54.97 247	89.86 292	43.03 414	75.02 345	86.32 341

COLMAP_ROB	66.92 17	73.01 312	70.41 327	80.81 251	87.13 234	65.63 198	88.30 151	84.19 306	62.96 343	63.80 397	87.69 229	38.04 400	92.56 208	46.66 400	74.91 346	84.24 376
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchT		68.46 358	67.85 349	70.29 387	80.70 373	43.93 431	72.47 404	74.88 402	60.15 370	70.55 325	76.57 408	49.94 310	81.59 383	50.58 375	74.83 347	85.34 360

pmmvs4		74.03 297	71.91 308	80.39 259	81.96 354	68.32 131	81.45 320	82.14 337	59.32 377	69.87 339	85.13 301	52.40 274	88.13 325	60.21 307	74.74 348	84.73 372

ITE_SJBPF				78.22 303	81.77 357	60.57 298		83.30 318	69.25 256	67.54 359	87.20 244	36.33 407	87.28 336	54.34 356	74.62 349	86.80 334

test0.0.03 1		68.00 361	67.69 354	68.90 393	77.55 400	47.43 416	75.70 388	72.95 412	66.66 294	66.56 374	82.29 360	48.06 328	75.87 416	44.97 411	74.51 350	83.41 386

test_0402		72.79 315	70.44 326	79.84 272	88.13 190	65.99 188	85.93 229	84.29 303	65.57 311	67.40 364	85.49 291	46.92 335	92.61 204	35.88 428	74.38 351	80.94 407

CP-MVSNet		78.22 222	78.34 197	77.84 311	87.83 206	54.54 377	87.94 164	91.17 132	77.65 46	73.48 294	88.49 206	62.24 173	88.43 321	62.19 288	74.07 352	90.55 210

FMVSNet5		69.50 347	67.96 347	74.15 356	82.97 338	55.35 369	80.01 344	82.12 338	62.56 350	63.02 398	81.53 366	36.92 403	81.92 382	48.42 390	74.06 353	85.17 365

MVS-HIRNet		59.14 390	57.67 392	63.57 408	81.65 358	43.50 432	71.73 406	65.06 431	39.59 433	51.43 428	57.73 436	38.34 398	82.58 378	39.53 421	73.95 354	64.62 432

tpmrst		72.39 316	72.13 307	73.18 367	80.54 375	49.91 410	79.91 346	79.08 376	63.11 340	71.69 317	79.95 384	55.32 245	82.77 377	65.66 260	73.89 355	86.87 332

PS-CasMVS		78.01 231	78.09 203	77.77 313	87.71 213	54.39 379	88.02 160	91.22 129	77.50 54	73.26 296	88.64 201	60.73 199	88.41 322	61.88 292	73.88 356	90.53 211

v148		78.72 211	77.80 213	81.47 230	82.73 342	61.96 280	86.30 220	88.08 234	73.26 170	76.18 237	85.47 292	62.46 168	92.36 219	71.92 200	73.82 357	90.09 232

Patchmatch-test		64.82 379	63.24 380	69.57 389	79.42 392	49.82 411	63.49 436	69.05 421	51.98 415	59.95 411	80.13 382	50.91 297	70.98 430	40.66 420	73.57 358	87.90 305

WR-MVS_H		78.51 217	78.49 192	78.56 296	88.02 196	56.38 354	88.43 143	92.67 68	77.14 64	73.89 288	87.55 234	66.25 125	89.24 305	58.92 319	73.55 359	90.06 236

AUN-MVS		79.21 199	77.60 221	84.05 147	88.71 168	67.61 153	85.84 233	87.26 257	69.08 262	77.23 209	88.14 220	53.20 269	93.47 165	75.50 165	73.45 360	91.06 187

hse-mvs2		81.72 133	80.94 139	84.07 142	88.72 167	67.68 151	85.87 231	87.26 257	76.02 96	84.67 80	88.22 215	61.54 183	93.48 164	82.71 88	73.44 361	91.06 187

testgi		66.67 369	66.53 366	67.08 403	75.62 409	41.69 438	75.93 384	76.50 395	66.11 303	65.20 388	86.59 263	35.72 409	74.71 423	43.71 412	73.38 362	84.84 370

Anonymous20240521		68.80 353	67.22 362	73.55 361	74.33 413	54.11 380	83.18 298	85.61 286	58.15 388	61.68 404	80.94 372	30.71 420	81.27 386	57.00 340	73.34 363	85.28 361

pm-mvs1		77.25 249	76.68 243	78.93 289	84.22 304	58.62 318	86.41 215	88.36 230	71.37 202	73.31 295	88.01 222	61.22 193	89.15 308	64.24 271	73.01 364	89.03 271

eth_miper_zixun_eth		77.92 233	76.69 242	81.61 228	83.00 335	61.98 279	83.15 299	89.20 201	69.52 249	74.86 275	84.35 317	61.76 179	92.56 208	71.50 203	72.89 365	90.28 223

miper_lstm_enhance		74.11 294	73.11 296	77.13 324	80.11 380	59.62 310	72.23 405	86.92 266	66.76 292	70.40 328	82.92 349	56.93 235	82.92 376	69.06 229	72.63 366	88.87 279

tpmvs		71.09 329	69.29 334	76.49 328	82.04 353	56.04 359	78.92 359	81.37 348	64.05 332	67.18 366	78.28 399	49.74 313	89.77 294	49.67 384	72.37 367	83.67 384

PEN-MVS		77.73 237	77.69 219	77.84 311	87.07 238	53.91 382	87.91 166	91.18 131	77.56 51	73.14 298	88.82 196	61.23 192	89.17 307	59.95 308	72.37 367	90.43 215

DSMNet-mixed		57.77 392	56.90 394	60.38 412	67.70 433	35.61 443	69.18 418	53.97 444	32.30 442	57.49 419	79.88 385	40.39 388	68.57 436	38.78 424	72.37 367	76.97 418

MonoMVSNet		76.49 263	75.80 252	78.58 295	81.55 361	58.45 319	86.36 218	86.22 277	74.87 126	74.73 277	83.73 332	51.79 289	88.73 316	70.78 208	72.15 370	88.55 293

IterMVS-SCA-FT		75.43 279	73.87 286	80.11 267	82.69 343	64.85 221	81.57 318	83.47 316	69.16 260	70.49 327	84.15 324	51.95 284	88.15 324	69.23 226	72.14 371	87.34 318

tpm cat1		70.57 335	68.31 341	77.35 321	82.41 350	57.95 328	78.08 371	80.22 364	52.04 413	68.54 352	77.66 404	52.00 283	87.84 329	51.77 368	72.07 372	86.25 342

RPSCF		73.23 309	71.46 313	78.54 297	82.50 347	59.85 307	82.18 311	82.84 332	58.96 381	71.15 324	89.41 184	45.48 356	84.77 363	58.82 321	71.83 373	91.02 191

IterMVS		74.29 290	72.94 298	78.35 302	81.53 362	63.49 252	81.58 317	82.49 334	68.06 281	69.99 336	83.69 334	51.66 291	85.54 354	65.85 258	71.64 374	86.01 349
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AllTest		70.96 330	68.09 345	79.58 279	85.15 283	63.62 245	84.58 266	79.83 367	62.31 352	60.32 409	86.73 253	32.02 415	88.96 313	50.28 379	71.57 375	86.15 345

TestCases				79.58 279	85.15 283	63.62 245		79.83 367	62.31 352	60.32 409	86.73 253	32.02 415	88.96 313	50.28 379	71.57 375	86.15 345

baseline1		76.98 252	76.75 241	77.66 314	88.13 190	55.66 365	85.12 251	81.89 340	73.04 175	76.79 219	88.90 193	62.43 169	87.78 330	63.30 277	71.18 377	89.55 256

Patchmtry		70.74 333	69.16 336	75.49 339	80.72 372	54.07 381	74.94 396	80.30 362	58.34 386	70.01 334	81.19 367	52.50 272	86.54 341	53.37 362	71.09 378	85.87 354

DTE-MVSNet		76.99 251	76.80 237	77.54 319	86.24 253	53.06 391	87.52 175	90.66 145	77.08 68	72.50 306	88.67 200	60.48 207	89.52 299	57.33 336	70.74 379	90.05 237

reproduce_monomvs		75.40 281	74.38 279	78.46 301	83.92 312	57.80 332	83.78 283	86.94 264	73.47 164	72.25 311	84.47 312	38.74 395	89.27 304	75.32 167	70.53 380	88.31 297

MIMVSNet1		68.58 355	66.78 365	73.98 358	80.07 381	51.82 397	80.77 329	84.37 300	64.40 325	59.75 412	82.16 362	36.47 406	83.63 370	42.73 415	70.33 381	86.48 340

pmmvs6		74.69 287	73.39 291	78.61 293	81.38 365	57.48 337	86.64 208	87.95 239	64.99 320	70.18 331	86.61 262	50.43 304	89.52 299	62.12 290	70.18 382	88.83 281

test_vis1_rt		60.28 388	58.42 391	65.84 405	67.25 434	55.60 366	70.44 414	60.94 438	44.33 427	59.00 413	66.64 428	24.91 428	68.67 435	62.80 279	69.48 383	73.25 424

TinyColmap		67.30 365	64.81 371	74.76 349	81.92 356	56.68 349	80.29 340	81.49 346	60.33 367	56.27 423	83.22 342	24.77 429	87.66 332	45.52 408	69.47 384	79.95 412

OurMVSNet-221017-0		74.26 291	72.42 304	79.80 273	83.76 316	59.59 311	85.92 230	86.64 269	66.39 301	66.96 368	87.58 231	39.46 390	91.60 247	65.76 259	69.27 385	88.22 299

JIA-IIPM		66.32 372	62.82 384	76.82 326	77.09 403	61.72 284	65.34 432	75.38 399	58.04 391	64.51 390	62.32 431	42.05 379	86.51 342	51.45 372	69.22 386	82.21 399

ADS-MVSNet2		66.20 375	63.33 379	74.82 348	79.92 382	58.75 317	67.55 424	75.19 400	53.37 410	65.25 386	75.86 412	42.32 375	80.53 390	41.57 418	68.91 387	85.18 363

ADS-MVSNet		64.36 380	62.88 383	68.78 395	79.92 382	47.17 419	67.55 424	71.18 414	53.37 410	65.25 386	75.86 412	42.32 375	73.99 426	41.57 418	68.91 387	85.18 363

test20.03		67.45 363	66.95 364	68.94 392	75.48 410	44.84 429	77.50 377	77.67 384	66.66 294	63.01 399	83.80 329	47.02 334	78.40 397	42.53 417	68.86 389	83.58 385

EU-MVSNet		68.53 357	67.61 356	71.31 382	78.51 398	47.01 420	84.47 268	84.27 304	42.27 429	66.44 379	84.79 309	40.44 387	83.76 368	58.76 322	68.54 390	83.17 388

dmvs_testset		62.63 384	64.11 375	58.19 414	78.55 397	24.76 452	75.28 390	65.94 429	67.91 282	60.34 408	76.01 411	53.56 264	73.94 427	31.79 432	67.65 391	75.88 421

our_test_3		69.14 350	67.00 363	75.57 336	79.80 386	58.80 316	77.96 373	77.81 383	59.55 375	62.90 401	78.25 400	47.43 330	83.97 367	51.71 369	67.58 392	83.93 381

ppachtmachnet_test		70.04 343	67.34 361	78.14 305	79.80 386	61.13 288	79.19 354	80.59 355	59.16 379	65.27 385	79.29 390	46.75 339	87.29 335	49.33 386	66.72 393	86.00 351

LF4IMVS		64.02 381	62.19 385	69.50 390	70.90 429	53.29 389	76.13 382	77.18 391	52.65 412	58.59 414	80.98 371	23.55 432	76.52 408	53.06 364	66.66 394	78.68 415

Patchmatch-RL test		70.24 340	67.78 353	77.61 316	77.43 401	59.57 312	71.16 409	70.33 415	62.94 344	68.65 350	72.77 421	50.62 301	85.49 355	69.58 224	66.58 395	87.77 308

dp		66.80 367	65.43 369	70.90 386	79.74 388	48.82 414	75.12 394	74.77 403	59.61 374	64.08 394	77.23 405	42.89 371	80.72 389	48.86 389	66.58 395	83.16 389

test_fmvs3		63.36 383	61.82 386	67.98 400	62.51 440	46.96 421	77.37 379	74.03 407	45.24 425	67.50 360	78.79 396	12.16 445	72.98 429	72.77 193	66.02 397	83.99 380

CL-MVSNet_self_test		72.37 318	71.46 313	75.09 344	79.49 391	53.53 384	80.76 330	85.01 295	69.12 261	70.51 326	82.05 363	57.92 223	84.13 366	52.27 367	66.00 398	87.60 311

FPMVS		53.68 398	51.64 400	59.81 413	65.08 437	51.03 404	69.48 417	69.58 419	41.46 430	40.67 437	72.32 422	16.46 441	70.00 434	24.24 441	65.42 399	58.40 437

pmmvs-eth3d		70.50 337	67.83 351	78.52 299	77.37 402	66.18 184	81.82 313	81.51 345	58.90 382	63.90 396	80.42 377	42.69 373	86.28 345	58.56 323	65.30 400	83.11 390

N_pmnet		52.79 400	53.26 398	51.40 424	78.99 395	7.68 458	69.52 416	3.89 457	51.63 416	57.01 420	74.98 416	40.83 385	65.96 439	37.78 425	64.67 401	80.56 411

PM-MVS		66.41 371	64.14 374	73.20 366	73.92 416	56.45 351	78.97 358	64.96 432	63.88 336	64.72 389	80.24 381	19.84 437	83.44 373	66.24 252	64.52 402	79.71 413

KD-MVS_self_test		68.81 352	67.59 357	72.46 373	74.29 414	45.45 423	77.93 374	87.00 262	63.12 339	63.99 395	78.99 395	42.32 375	84.77 363	56.55 346	64.09 403	87.16 325

SixPastTwentyTwo		73.37 304	71.26 318	79.70 275	85.08 286	57.89 329	85.57 237	83.56 314	71.03 212	65.66 382	85.88 280	42.10 378	92.57 207	59.11 317	63.34 404	88.65 289

sc_t1		72.19 321	69.51 332	80.23 264	84.81 291	61.09 290	84.68 261	80.22 364	60.70 365	71.27 321	83.58 337	36.59 405	89.24 305	60.41 304	63.31 405	90.37 218

tt0320		70.49 338	68.03 346	77.89 309	84.78 292	59.12 315	83.55 291	80.44 359	58.13 389	67.43 363	80.41 378	39.26 392	87.54 333	55.12 351	63.18 406	86.99 330

EGC-MVSNET		52.07 402	47.05 406	67.14 402	83.51 321	60.71 296	80.50 336	67.75 424	0.07 452	0.43 453	75.85 414	24.26 430	81.54 384	28.82 435	62.25 407	59.16 435

TransMVSNet (Re)		75.39 282	74.56 275	77.86 310	85.50 274	57.10 342	86.78 203	86.09 281	72.17 188	71.53 319	87.34 238	63.01 162	89.31 303	56.84 342	61.83 408	87.17 323

MDA-MVSNet_test_wron		65.03 377	62.92 381	71.37 379	75.93 405	56.73 346	69.09 421	74.73 404	57.28 397	54.03 426	77.89 401	45.88 348	74.39 425	49.89 383	61.55 409	82.99 393

YYNet1		65.03 377	62.91 382	71.38 378	75.85 407	56.60 350	69.12 420	74.66 406	57.28 397	54.12 425	77.87 402	45.85 349	74.48 424	49.95 382	61.52 410	83.05 391

mvsany_test1		62.30 385	61.26 389	65.41 406	69.52 430	54.86 374	66.86 426	49.78 446	46.65 423	68.50 353	83.21 343	49.15 321	66.28 438	56.93 341	60.77 411	75.11 422

ambc				75.24 343	73.16 423	50.51 408	63.05 437	87.47 252		64.28 391	77.81 403	17.80 439	89.73 296	57.88 331	60.64 412	85.49 357

TDRefinement		67.49 362	64.34 373	76.92 325	73.47 421	61.07 291	84.86 258	82.98 328	59.77 373	58.30 416	85.13 301	26.06 425	87.89 328	47.92 397	60.59 413	81.81 403

Gipuma		45.18 409	41.86 412	55.16 421	77.03 404	51.52 400	32.50 445	80.52 356	32.46 441	27.12 444	35.02 445	9.52 448	75.50 418	22.31 442	60.21 414	38.45 444
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tt0320-xc		70.11 342	67.45 359	78.07 307	85.33 278	59.51 313	83.28 296	78.96 377	58.77 383	67.10 367	80.28 380	36.73 404	87.42 334	56.83 343	59.77 415	87.29 320

new-patchmatchnet		61.73 386	61.73 387	61.70 410	72.74 426	24.50 453	69.16 419	78.03 382	61.40 360	56.72 421	75.53 415	38.42 397	76.48 409	45.95 406	57.67 416	84.13 378

MDA-MVSNet-bldmvs		66.68 368	63.66 378	75.75 333	79.28 393	60.56 299	73.92 401	78.35 381	64.43 324	50.13 431	79.87 386	44.02 365	83.67 369	46.10 405	56.86 417	83.03 392

new_pmnet		50.91 403	50.29 403	52.78 423	68.58 432	34.94 445	63.71 434	56.63 443	39.73 432	44.95 434	65.47 429	21.93 434	58.48 443	34.98 429	56.62 418	64.92 431

test_f		52.09 401	50.82 402	55.90 418	53.82 448	42.31 437	59.42 438	58.31 442	36.45 437	56.12 424	70.96 425	12.18 444	57.79 444	53.51 361	56.57 419	67.60 429

test_vis3_rt		49.26 405	47.02 407	56.00 417	54.30 446	45.27 427	66.76 428	48.08 447	36.83 436	44.38 435	53.20 440	7.17 452	64.07 440	56.77 344	55.66 420	58.65 436

PMVS	37.38 22	44.16 410	40.28 414	55.82 419	40.82 454	42.54 436	65.12 433	63.99 434	34.43 439	24.48 445	57.12 438	3.92 455	76.17 413	17.10 446	55.52 421	48.75 440
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
APD_test1		53.31 399	49.93 404	63.42 409	65.68 436	50.13 409	71.59 408	66.90 427	34.43 439	40.58 438	71.56 424	8.65 450	76.27 411	34.64 430	55.36 422	63.86 433

mvs5depth		69.45 348	67.45 359	75.46 340	73.93 415	55.83 362	79.19 354	83.23 320	66.89 289	71.63 318	83.32 341	33.69 413	85.09 359	59.81 310	55.34 423	85.46 358

pmmvs3		57.79 391	54.26 396	68.37 397	64.02 439	56.72 347	75.12 394	65.17 430	40.20 431	52.93 427	69.86 427	20.36 436	75.48 419	45.45 409	55.25 424	72.90 425

UnsupCasMVSNet_eth		67.33 364	65.99 368	71.37 379	73.48 420	51.47 401	75.16 392	85.19 290	65.20 315	60.78 407	80.93 374	42.35 374	77.20 403	57.12 337	53.69 425	85.44 359

K. test v3		71.19 327	68.51 339	79.21 285	83.04 334	57.78 333	84.35 275	76.91 393	72.90 178	62.99 400	82.86 351	39.27 391	91.09 273	61.65 295	52.66 426	88.75 285

mmtdpeth		74.16 293	73.01 297	77.60 318	83.72 317	61.13 288	85.10 252	85.10 292	72.06 190	77.21 213	80.33 379	43.84 366	85.75 350	77.14 144	52.61 427	85.91 352

UnsupCasMVSNet_bld		63.70 382	61.53 388	70.21 388	73.69 418	51.39 402	72.82 403	81.89 340	55.63 404	57.81 418	71.80 423	38.67 396	78.61 396	49.26 387	52.21 428	80.63 409

LCM-MVSNet		54.25 395	49.68 405	67.97 401	53.73 449	45.28 426	66.85 427	80.78 352	35.96 438	39.45 439	62.23 432	8.70 449	78.06 400	48.24 394	51.20 429	80.57 410

KD-MVS_2432*1600		66.22 373	63.89 376	73.21 364	75.47 411	53.42 386	70.76 412	84.35 301	64.10 330	66.52 376	78.52 397	34.55 411	84.98 360	50.40 377	50.33 430	81.23 405

miper_refine_blended		66.22 373	63.89 376	73.21 364	75.47 411	53.42 386	70.76 412	84.35 301	64.10 330	66.52 376	78.52 397	34.55 411	84.98 360	50.40 377	50.33 430	81.23 405

mvsany_test3		53.99 396	51.45 401	61.61 411	55.51 445	44.74 430	63.52 435	45.41 450	43.69 428	58.11 417	76.45 409	17.99 438	63.76 441	54.77 354	47.59 432	76.34 420

lessismore_v0				78.97 288	81.01 371	57.15 341		65.99 428		61.16 406	82.82 352	39.12 393	91.34 263	59.67 311	46.92 433	88.43 295

testf1		45.72 406	41.96 410	57.00 415	56.90 443	45.32 424	66.14 429	59.26 440	26.19 443	30.89 442	60.96 434	4.14 453	70.64 432	26.39 439	46.73 434	55.04 438

APD_test2		45.72 406	41.96 410	57.00 415	56.90 443	45.32 424	66.14 429	59.26 440	26.19 443	30.89 442	60.96 434	4.14 453	70.64 432	26.39 439	46.73 434	55.04 438

ttmdpeth		59.91 389	57.10 393	68.34 398	67.13 435	46.65 422	74.64 397	67.41 425	48.30 421	62.52 403	85.04 305	20.40 435	75.93 415	42.55 416	45.90 436	82.44 397

MVStest1		56.63 393	52.76 399	68.25 399	61.67 441	53.25 390	71.67 407	68.90 423	38.59 434	50.59 430	83.05 346	25.08 427	70.66 431	36.76 427	38.56 437	80.83 408

PVSNet_0	57.27 20	61.67 387	59.27 390	68.85 394	79.61 389	57.44 338	68.01 422	73.44 409	55.93 403	58.54 415	70.41 426	44.58 360	77.55 402	47.01 399	35.91 438	71.55 426

WB-MVS		54.94 394	54.72 395	55.60 420	73.50 419	20.90 454	74.27 400	61.19 437	59.16 379	50.61 429	74.15 417	47.19 333	75.78 417	17.31 445	35.07 439	70.12 427

test_method		31.52 414	29.28 418	38.23 428	27.03 456	6.50 459	20.94 447	62.21 436	4.05 450	22.35 448	52.50 441	13.33 442	47.58 448	27.04 438	34.04 440	60.62 434

SSC-MVS		53.88 397	53.59 397	54.75 422	72.87 425	19.59 455	73.84 402	60.53 439	57.58 395	49.18 433	73.45 420	46.34 344	75.47 420	16.20 448	32.28 441	69.20 428

PMMVS2		40.82 411	38.86 415	46.69 425	53.84 447	16.45 456	48.61 442	49.92 445	37.49 435	31.67 440	60.97 433	8.14 451	56.42 445	28.42 436	30.72 442	67.19 430

dongtai		45.42 408	45.38 409	45.55 426	73.36 422	26.85 450	67.72 423	34.19 452	54.15 408	49.65 432	56.41 439	25.43 426	62.94 442	19.45 443	28.09 443	46.86 442

kuosan		39.70 412	40.40 413	37.58 429	64.52 438	26.98 448	65.62 431	33.02 453	46.12 424	42.79 436	48.99 442	24.10 431	46.56 450	12.16 451	26.30 444	39.20 443

DeepMVS_CX				27.40 432	40.17 455	26.90 449		24.59 456	17.44 448	23.95 446	48.61 443	9.77 447	26.48 451	18.06 444	24.47 445	28.83 445

MVE	26.22 23	30.37 416	25.89 420	43.81 427	44.55 453	35.46 444	28.87 446	39.07 451	18.20 447	18.58 449	40.18 444	2.68 456	47.37 449	17.07 447	23.78 446	48.60 441
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		31.77 413	30.64 416	35.15 430	52.87 450	27.67 447	57.09 440	47.86 448	24.64 445	16.40 450	33.05 446	11.23 446	54.90 446	14.46 449	18.15 447	22.87 446

EMVS		30.81 415	29.65 417	34.27 431	50.96 451	25.95 451	56.58 441	46.80 449	24.01 446	15.53 451	30.68 447	12.47 443	54.43 447	12.81 450	17.05 448	22.43 447

ANet_high		50.57 404	46.10 408	63.99 407	48.67 452	39.13 440	70.99 411	80.85 351	61.39 361	31.18 441	57.70 437	17.02 440	73.65 428	31.22 434	15.89 449	79.18 414

tmp_tt		18.61 418	21.40 421	10.23 434	4.82 457	10.11 457	34.70 444	30.74 455	1.48 451	23.91 447	26.07 448	28.42 423	13.41 453	27.12 437	15.35 450	7.17 448

wuyk23d		16.82 419	15.94 422	19.46 433	58.74 442	31.45 446	39.22 443	3.74 458	6.84 449	6.04 452	2.70 452	1.27 457	24.29 452	10.54 452	14.40 451	2.63 449

testmvs		6.04 422	8.02 425	0.10 436	0.08 458	0.03 461	69.74 415	0.04 459	0.05 453	0.31 454	1.68 453	0.02 459	0.04 454	0.24 453	0.02 452	0.25 451

test123		6.12 421	8.11 424	0.14 435	0.06 459	0.09 460	71.05 410	0.03 460	0.04 454	0.25 455	1.30 454	0.05 458	0.03 455	0.21 454	0.01 453	0.29 450

mmdepth		0.00 424	0.00 427	0.00 437	0.00 460	0.00 462	0.00 448	0.00 461	0.00 455	0.00 456	0.00 455	0.00 460	0.00 456	0.00 455	0.00 454	0.00 452

monomultidepth		0.00 424	0.00 427	0.00 437	0.00 460	0.00 462	0.00 448	0.00 461	0.00 455	0.00 456	0.00 455	0.00 460	0.00 456	0.00 455	0.00 454	0.00 452

test_blank		0.00 424	0.00 427	0.00 437	0.00 460	0.00 462	0.00 448	0.00 461	0.00 455	0.00 456	0.00 455	0.00 460	0.00 456	0.00 455	0.00 454	0.00 452

uanet_test		0.00 424	0.00 427	0.00 437	0.00 460	0.00 462	0.00 448	0.00 461	0.00 455	0.00 456	0.00 455	0.00 460	0.00 456	0.00 455	0.00 454	0.00 452

DCPMVS		0.00 424	0.00 427	0.00 437	0.00 460	0.00 462	0.00 448	0.00 461	0.00 455	0.00 456	0.00 455	0.00 460	0.00 456	0.00 455	0.00 454	0.00 452

cdsmvs_eth3d_5k		19.96 417	26.61 419	0.00 437	0.00 460	0.00 462	0.00 448	89.26 198	0.00 455	0.00 456	88.61 202	61.62 182	0.00 456	0.00 455	0.00 454	0.00 452

pcd_1.5k_mvsjas		5.26 423	7.02 426	0.00 437	0.00 460	0.00 462	0.00 448	0.00 461	0.00 455	0.00 456	0.00 455	63.15 158	0.00 456	0.00 455	0.00 454	0.00 452

sosnet-low-res		0.00 424	0.00 427	0.00 437	0.00 460	0.00 462	0.00 448	0.00 461	0.00 455	0.00 456	0.00 455	0.00 460	0.00 456	0.00 455	0.00 454	0.00 452

sosnet		0.00 424	0.00 427	0.00 437	0.00 460	0.00 462	0.00 448	0.00 461	0.00 455	0.00 456	0.00 455	0.00 460	0.00 456	0.00 455	0.00 454	0.00 452

uncertanet		0.00 424	0.00 427	0.00 437	0.00 460	0.00 462	0.00 448	0.00 461	0.00 455	0.00 456	0.00 455	0.00 460	0.00 456	0.00 455	0.00 454	0.00 452

Regformer		0.00 424	0.00 427	0.00 437	0.00 460	0.00 462	0.00 448	0.00 461	0.00 455	0.00 456	0.00 455	0.00 460	0.00 456	0.00 455	0.00 454	0.00 452

ab-mvs-re		7.23 420	9.64 423	0.00 437	0.00 460	0.00 462	0.00 448	0.00 461	0.00 455	0.00 456	86.72 255	0.00 460	0.00 456	0.00 455	0.00 454	0.00 452

uanet		0.00 424	0.00 427	0.00 437	0.00 460	0.00 462	0.00 448	0.00 461	0.00 455	0.00 456	0.00 455	0.00 460	0.00 456	0.00 455	0.00 454	0.00 452

WAC-MVS						42.58 434								39.46 422

FOURS1					95.00 10	72.39 41	95.06 1	93.84 16	74.49 134	91.30 15

test_one_0601					95.07 7	71.46 59		94.14 6	78.27 41	92.05 11	95.74 6	80.83 11

eth-test2					0.00 460

eth-test					0.00 460

test_241102_ONE					95.30 2	70.98 68		94.06 11	77.17 63	93.10 1	95.39 16	82.99 1	97.27 12

save fliter					93.80 40	72.35 44	90.47 69	91.17 132	74.31 139

test0726					95.27 5	71.25 61	93.60 7	94.11 7	77.33 57	92.81 3	95.79 3	80.98 9

GSMVS																88.96 276

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 293				88.96 276

sam_mvs												50.01 308

MTGPA								92.02 98

test_post1							78.90 360				5.43 451	48.81 327	85.44 357	59.25 315

test_post											5.46 450	50.36 305	84.24 365

patchmatchnet-post											74.00 418	51.12 296	88.60 319

MTMP							92.18 35	32.83 454

gm-plane-assit					81.40 364	53.83 383			62.72 349		80.94 372		92.39 217	63.40 276

TEST9					93.26 52	72.96 25	88.75 131	91.89 106	68.44 276	85.00 73	93.10 81	74.36 29	95.41 76

test_8					93.13 56	72.57 35	88.68 136	91.84 110	68.69 271	84.87 77	93.10 81	74.43 27	95.16 86

agg_prior					92.85 64	71.94 52		91.78 113		84.41 88			94.93 97

test_prior4						72.60 34	89.01 118

test_prior				86.33 60	92.61 70	69.59 94		92.97 55					95.48 70			93.91 67

旧先验2							86.56 211		58.10 390	87.04 55			88.98 311	74.07 178

新几何2							86.29 221

无先验							87.48 176	88.98 211	60.00 371				94.12 131	67.28 245		88.97 275

原ACMM2							86.86 199

testdata2													91.01 275	62.37 286

segment_acmp												73.08 40

testdata1							84.14 279		75.71 100

plane_prior7					90.08 112	68.51 127

plane_prior6					89.84 121	68.70 121						60.42 208

plane_prior4											91.00 144

plane_prior3						68.60 124			78.44 36	78.92 170

plane_prior2							91.25 55		79.12 28

plane_prior1					89.90 120

n2								0.00 461

nn								0.00 461

door-mid								69.98 417

test11								92.23 88

door								69.44 420

HQP5-MVS						66.98 173

HQP-NCC					89.33 140		89.17 109		76.41 85	77.23 209

ACMP_Plane					89.33 140		89.17 109		76.41 85	77.23 209

BP-MVS														77.47 139

HQP4-MVS										77.24 208			95.11 90			91.03 189

HQP2-MVS												60.17 211

NP-MVS					89.62 125	68.32 131					90.24 157

MDTV_nov1_ep13_2view						37.79 442	75.16 392		55.10 405	66.53 375		49.34 318		53.98 358		87.94 304

Test By Simon												64.33 144

High-resolution multi-view benchmark